Metafeature description

In this example, we will show you how to list the types of metafeatures, groups, and summaries available.

from pymfe.mfe import MFE

This function shows the description of all metafeatures.

+-------------+-------------------+--------------------------------------------+
|    Group    | Meta-feature name |                Description                 |
+=============+===================+============================================+
| clustering  | ch                | Compute the Calinski and Harabasz index.   |
+-------------+-------------------+--------------------------------------------+
| clustering  | int               | Compute the INT index.                     |
+-------------+-------------------+--------------------------------------------+
| clustering  | nre               | Compute the normalized relative entropy.   |
+-------------+-------------------+--------------------------------------------+
| clustering  | pb                | Compute the pearson correlation between    |
|             |                   | class matching and instance distances.     |
+-------------+-------------------+--------------------------------------------+
| clustering  | sc                | Compute the number of clusters with size   |
|             |                   | smaller than a given size.                 |
+-------------+-------------------+--------------------------------------------+
| clustering  | sil               | Compute the mean silhouette value.         |
+-------------+-------------------+--------------------------------------------+
| clustering  | vdb               | Compute the Davies and Bouldin Index.      |
+-------------+-------------------+--------------------------------------------+
| clustering  | vdu               | Compute the Dunn Index.                    |
+-------------+-------------------+--------------------------------------------+
| complexity  | c1                | Compute the entropy of class proportions.  |
+-------------+-------------------+--------------------------------------------+
| complexity  | c2                | Compute the imbalance ratio.               |
+-------------+-------------------+--------------------------------------------+
| complexity  | cls_coef          | Clustering coefficient.                    |
+-------------+-------------------+--------------------------------------------+
| complexity  | density           | Average density of the network.            |
+-------------+-------------------+--------------------------------------------+
| complexity  | f1                | Maximum Fisher's discriminant ratio.       |
+-------------+-------------------+--------------------------------------------+
| complexity  | f1v               | Directional-vector maximum Fisher's        |
|             |                   | discriminant ratio.                        |
+-------------+-------------------+--------------------------------------------+
| complexity  | f2                | Volume of the overlapping region.          |
+-------------+-------------------+--------------------------------------------+
| complexity  | f3                | Compute feature maximum individual         |
|             |                   | efficiency.                                |
+-------------+-------------------+--------------------------------------------+
| complexity  | f4                | Compute the collective feature efficiency. |
+-------------+-------------------+--------------------------------------------+
| complexity  | hubs              | Hub score.                                 |
+-------------+-------------------+--------------------------------------------+
| complexity  | l1                | Sum of error distance by linear            |
|             |                   | programming.                               |
+-------------+-------------------+--------------------------------------------+
| complexity  | l2                | Compute the OVO subsets error rate of      |
|             |                   | linear classifier.                         |
+-------------+-------------------+--------------------------------------------+
| complexity  | l3                | Non-Linearity of a linear classifier.      |
+-------------+-------------------+--------------------------------------------+
| complexity  | lsc               | Local set average cardinality.             |
+-------------+-------------------+--------------------------------------------+
| complexity  | n1                | Compute the fraction of borderline points. |
+-------------+-------------------+--------------------------------------------+
| complexity  | n2                | Ratio of intra and extra class nearest     |
|             |                   | neighbor distance.                         |
+-------------+-------------------+--------------------------------------------+
| complexity  | n3                | Error rate of the nearest neighbor         |
|             |                   | classifier.                                |
+-------------+-------------------+--------------------------------------------+
| complexity  | n4                | Compute the non-linearity of the k-NN      |
|             |                   | Classifier.                                |
+-------------+-------------------+--------------------------------------------+
| complexity  | t1                | Fraction of hyperspheres covering data.    |
+-------------+-------------------+--------------------------------------------+
| complexity  | t2                | Compute the average number of features per |
|             |                   | dimension.                                 |
+-------------+-------------------+--------------------------------------------+
| complexity  | t3                | Compute the average number of PCA          |
|             |                   | dimensions per points.                     |
+-------------+-------------------+--------------------------------------------+
| complexity  | t4                | Compute the ratio of the PCA dimension to  |
|             |                   | the original dimension.                    |
+-------------+-------------------+--------------------------------------------+
| concept     | cohesiveness      | Compute the improved version of the        |
|             |                   | weighted distance, that captures how dense |
|             |                   | or sparse is the example distribution.     |
+-------------+-------------------+--------------------------------------------+
| concept     | conceptvar        | Compute the concept variation that         |
|             |                   | estimates the variability of class labels  |
|             |                   | among examples.                            |
+-------------+-------------------+--------------------------------------------+
| concept     | impconceptvar     | Compute the improved concept variation     |
|             |                   | that estimates the variability of class    |
|             |                   | labels among examples.                     |
+-------------+-------------------+--------------------------------------------+
| concept     | wg_dist           | Compute the weighted distance, that        |
|             |                   | captures how dense or sparse is the        |
|             |                   | example distribution.                      |
+-------------+-------------------+--------------------------------------------+
| info-theory | attr_conc         | Compute concentration coef. of each pair   |
|             |                   | of distinct attributes.                    |
+-------------+-------------------+--------------------------------------------+
| info-theory | attr_ent          | Compute Shannon's entropy for each         |
|             |                   | predictive attribute.                      |
+-------------+-------------------+--------------------------------------------+
| info-theory | class_conc        | Compute concentration coefficient between  |
|             |                   | each attribute and class.                  |
+-------------+-------------------+--------------------------------------------+
| info-theory | class_ent         | Compute target attribute Shannon's         |
|             |                   | entropy.                                   |
+-------------+-------------------+--------------------------------------------+
| info-theory | eq_num_attr       | Compute the number of attributes           |
|             |                   | equivalent for a predictive task.          |
+-------------+-------------------+--------------------------------------------+
| info-theory | joint_ent         | Compute the joint entropy between each     |
|             |                   | attribute and class.                       |
+-------------+-------------------+--------------------------------------------+
| info-theory | mut_inf           | Compute the mutual information between     |
|             |                   | each attribute and target.                 |
+-------------+-------------------+--------------------------------------------+
| info-theory | ns_ratio          | Compute the noisiness of attributes.       |
+-------------+-------------------+--------------------------------------------+
| landmarking | best_node         | Performance of a the best single decision  |
|             |                   | tree node.                                 |
+-------------+-------------------+--------------------------------------------+
| landmarking | elite_nn          | Performance of Elite Nearest Neighbor.     |
+-------------+-------------------+--------------------------------------------+
| landmarking | linear_discr      | Performance of the Linear Discriminant     |
|             |                   | classifier.                                |
+-------------+-------------------+--------------------------------------------+
| landmarking | naive_bayes       | Performance of the Naive Bayes classifier. |
+-------------+-------------------+--------------------------------------------+
| landmarking | one_nn            | Performance of the 1-Nearest Neighbor      |
|             |                   | classifier.                                |
+-------------+-------------------+--------------------------------------------+
| landmarking | random_node       | Performance of the single decision tree    |
|             |                   | node model induced by a random attribute.  |
+-------------+-------------------+--------------------------------------------+
| landmarking | worst_node        | Performance of the single decision tree    |
|             |                   | node model induced by the worst            |
|             |                   | informative attribute.                     |
+-------------+-------------------+--------------------------------------------+
| general     | attr_to_inst      | Compute the ratio between the number of    |
|             |                   | attributes.                                |
+-------------+-------------------+--------------------------------------------+
| general     | cat_to_num        | Compute the ratio between the number of    |
|             |                   | categoric and numeric features.            |
+-------------+-------------------+--------------------------------------------+
| general     | freq_class        | Compute the relative frequency of each     |
|             |                   | distinct class.                            |
+-------------+-------------------+--------------------------------------------+
| general     | inst_to_attr      | Compute the ratio between the number of    |
|             |                   | instances and attributes.                  |
+-------------+-------------------+--------------------------------------------+
| general     | nr_attr           | Compute the total number of attributes.    |
+-------------+-------------------+--------------------------------------------+
| general     | nr_bin            | Compute the number of binary attributes.   |
+-------------+-------------------+--------------------------------------------+
| general     | nr_cat            | Compute the number of categorical          |
|             |                   | attributes.                                |
+-------------+-------------------+--------------------------------------------+
| general     | nr_class          | Compute the number of distinct classes.    |
+-------------+-------------------+--------------------------------------------+
| general     | nr_inst           | Compute the number of instances (rows) in  |
|             |                   | the dataset.                               |
+-------------+-------------------+--------------------------------------------+
| general     | nr_num            | Compute the number of numeric features.    |
+-------------+-------------------+--------------------------------------------+
| general     | num_to_cat        | Compute the number of numerical and        |
|             |                   | categorical features.                      |
+-------------+-------------------+--------------------------------------------+
| statistical | can_cor           | Compute canonical correlations of data.    |
+-------------+-------------------+--------------------------------------------+
| statistical | cor               | Compute the absolute value of the          |
|             |                   | correlation of distinct dataset column     |
|             |                   | pairs.                                     |
+-------------+-------------------+--------------------------------------------+
| statistical | cov               | Compute the absolute value of the          |
|             |                   | covariance of distinct dataset attribute   |
|             |                   | pairs.                                     |
+-------------+-------------------+--------------------------------------------+
| statistical | eigenvalues       | Compute the eigenvalues of covariance      |
|             |                   | matrix from dataset.                       |
+-------------+-------------------+--------------------------------------------+
| statistical | g_mean            | Compute the geometric mean of each         |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | gravity           | Compute the distance between minority and  |
|             |                   | majority classes center of mass.           |
+-------------+-------------------+--------------------------------------------+
| statistical | h_mean            | Compute the harmonic mean of each          |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | iq_range          | Compute the interquartile range (IQR) of   |
|             |                   | each attribute.                            |
+-------------+-------------------+--------------------------------------------+
| statistical | kurtosis          | Compute the kurtosis of each attribute.    |
+-------------+-------------------+--------------------------------------------+
| statistical | lh_trace          | Compute the Lawley-Hotelling trace.        |
+-------------+-------------------+--------------------------------------------+
| statistical | mad               | Compute the Median Absolute Deviation      |
|             |                   | (MAD) adjusted by a factor.                |
+-------------+-------------------+--------------------------------------------+
| statistical | max               | Compute the maximum value from each        |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | mean              | Compute the mean value of each attribute.  |
+-------------+-------------------+--------------------------------------------+
| statistical | median            | Compute the median value from each         |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | min               | Compute the minimum value from each        |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | nr_cor_attr       | Compute the number of distinct highly      |
|             |                   | correlated pair of attributes.             |
+-------------+-------------------+--------------------------------------------+
| statistical | nr_disc           | Compute the number of canonical            |
|             |                   | correlation between each attribute and     |
|             |                   | class.                                     |
+-------------+-------------------+--------------------------------------------+
| statistical | nr_norm           | Compute the number of attributes normally  |
|             |                   | distributed based in a given method.       |
+-------------+-------------------+--------------------------------------------+
| statistical | nr_outliers       | Compute the number of attributes with at   |
|             |                   | least one outlier value.                   |
+-------------+-------------------+--------------------------------------------+
| statistical | p_trace           | Compute the Pillai's trace.                |
+-------------+-------------------+--------------------------------------------+
| statistical | range             | Compute the range (max - min) of each      |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | roy_root          | Compute the Roy's largest root.            |
+-------------+-------------------+--------------------------------------------+
| statistical | sd                | Compute the standard deviation of each     |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | sd_ratio          | Compute a statistical test for homogeneity |
|             |                   | of covariances.                            |
+-------------+-------------------+--------------------------------------------+
| statistical | skewness          | Compute the skewness for each attribute.   |
+-------------+-------------------+--------------------------------------------+
| statistical | sparsity          | Compute (possibly normalized) sparsity     |
|             |                   | metric for each attribute.                 |
+-------------+-------------------+--------------------------------------------+
| statistical | t_mean            | Compute the trimmed mean of each           |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | var               | Compute the variance of each attribute.    |
+-------------+-------------------+--------------------------------------------+
| statistical | w_lambda          | Compute the Wilks' Lambda value.           |
+-------------+-------------------+--------------------------------------------+
| model-based | leaves            | Compute the number of leaf nodes in the DT |
|             |                   | model.                                     |
+-------------+-------------------+--------------------------------------------+
| model-based | leaves_branch     | Compute the size of branches in the DT     |
|             |                   | model.                                     |
+-------------+-------------------+--------------------------------------------+
| model-based | leaves_corrob     | Compute the leaves corroboration of the DT |
|             |                   | model.                                     |
+-------------+-------------------+--------------------------------------------+
| model-based | leaves_homo       | Compute the DT model Homogeneity for every |
|             |                   | leaf node.                                 |
+-------------+-------------------+--------------------------------------------+
| model-based | leaves_per_class  | Compute the proportion of leaves per class |
|             |                   | in DT model.                               |
+-------------+-------------------+--------------------------------------------+
| model-based | nodes             | Compute the number of non-leaf nodes in DT |
|             |                   | model.                                     |
+-------------+-------------------+--------------------------------------------+
| model-based | nodes_per_attr    | Compute the ratio of nodes per number of   |
|             |                   | attributes in DT model.                    |
+-------------+-------------------+--------------------------------------------+
| model-based | nodes_per_inst    | Compute the ratio of non-leaf nodes per    |
|             |                   | number of instances in DT model.           |
+-------------+-------------------+--------------------------------------------+
| model-based | nodes_per_level   | Compute the ratio of number of nodes per   |
|             |                   | tree level in DT model.                    |
+-------------+-------------------+--------------------------------------------+
| model-based | nodes_repeated    | Compute the number of repeated nodes in DT |
|             |                   | model.                                     |
+-------------+-------------------+--------------------------------------------+
| model-based | tree_depth        | Compute the depth of every node in the DT  |
|             |                   | model.                                     |
+-------------+-------------------+--------------------------------------------+
| model-based | tree_imbalance    | Compute the tree imbalance for each leaf   |
|             |                   | node.                                      |
+-------------+-------------------+--------------------------------------------+
| model-based | tree_shape        | Compute the tree shape for every leaf      |
|             |                   | node.                                      |
+-------------+-------------------+--------------------------------------------+
| model-based | var_importance    | Compute the features importance of the DT  |
|             |                   | model for each attribute.                  |
+-------------+-------------------+--------------------------------------------+
| itemset     | one_itemset       | Compute the one itemset meta-feature.      |
+-------------+-------------------+--------------------------------------------+
| itemset     | two_itemset       | Compute the two itemset meta-feature.      |
+-------------+-------------------+--------------------------------------------+

You can select a specific group.

MFE.metafeature_description(groups=["general", "statistical"])
+-------------+-------------------+--------------------------------------------+
|    Group    | Meta-feature name |                Description                 |
+=============+===================+============================================+
| general     | attr_to_inst      | Compute the ratio between the number of    |
|             |                   | attributes.                                |
+-------------+-------------------+--------------------------------------------+
| general     | cat_to_num        | Compute the ratio between the number of    |
|             |                   | categoric and numeric features.            |
+-------------+-------------------+--------------------------------------------+
| general     | freq_class        | Compute the relative frequency of each     |
|             |                   | distinct class.                            |
+-------------+-------------------+--------------------------------------------+
| general     | inst_to_attr      | Compute the ratio between the number of    |
|             |                   | instances and attributes.                  |
+-------------+-------------------+--------------------------------------------+
| general     | nr_attr           | Compute the total number of attributes.    |
+-------------+-------------------+--------------------------------------------+
| general     | nr_bin            | Compute the number of binary attributes.   |
+-------------+-------------------+--------------------------------------------+
| general     | nr_cat            | Compute the number of categorical          |
|             |                   | attributes.                                |
+-------------+-------------------+--------------------------------------------+
| general     | nr_class          | Compute the number of distinct classes.    |
+-------------+-------------------+--------------------------------------------+
| general     | nr_inst           | Compute the number of instances (rows) in  |
|             |                   | the dataset.                               |
+-------------+-------------------+--------------------------------------------+
| general     | nr_num            | Compute the number of numeric features.    |
+-------------+-------------------+--------------------------------------------+
| general     | num_to_cat        | Compute the number of numerical and        |
|             |                   | categorical features.                      |
+-------------+-------------------+--------------------------------------------+
| statistical | can_cor           | Compute canonical correlations of data.    |
+-------------+-------------------+--------------------------------------------+
| statistical | cor               | Compute the absolute value of the          |
|             |                   | correlation of distinct dataset column     |
|             |                   | pairs.                                     |
+-------------+-------------------+--------------------------------------------+
| statistical | cov               | Compute the absolute value of the          |
|             |                   | covariance of distinct dataset attribute   |
|             |                   | pairs.                                     |
+-------------+-------------------+--------------------------------------------+
| statistical | eigenvalues       | Compute the eigenvalues of covariance      |
|             |                   | matrix from dataset.                       |
+-------------+-------------------+--------------------------------------------+
| statistical | g_mean            | Compute the geometric mean of each         |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | gravity           | Compute the distance between minority and  |
|             |                   | majority classes center of mass.           |
+-------------+-------------------+--------------------------------------------+
| statistical | h_mean            | Compute the harmonic mean of each          |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | iq_range          | Compute the interquartile range (IQR) of   |
|             |                   | each attribute.                            |
+-------------+-------------------+--------------------------------------------+
| statistical | kurtosis          | Compute the kurtosis of each attribute.    |
+-------------+-------------------+--------------------------------------------+
| statistical | lh_trace          | Compute the Lawley-Hotelling trace.        |
+-------------+-------------------+--------------------------------------------+
| statistical | mad               | Compute the Median Absolute Deviation      |
|             |                   | (MAD) adjusted by a factor.                |
+-------------+-------------------+--------------------------------------------+
| statistical | max               | Compute the maximum value from each        |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | mean              | Compute the mean value of each attribute.  |
+-------------+-------------------+--------------------------------------------+
| statistical | median            | Compute the median value from each         |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | min               | Compute the minimum value from each        |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | nr_cor_attr       | Compute the number of distinct highly      |
|             |                   | correlated pair of attributes.             |
+-------------+-------------------+--------------------------------------------+
| statistical | nr_disc           | Compute the number of canonical            |
|             |                   | correlation between each attribute and     |
|             |                   | class.                                     |
+-------------+-------------------+--------------------------------------------+
| statistical | nr_norm           | Compute the number of attributes normally  |
|             |                   | distributed based in a given method.       |
+-------------+-------------------+--------------------------------------------+
| statistical | nr_outliers       | Compute the number of attributes with at   |
|             |                   | least one outlier value.                   |
+-------------+-------------------+--------------------------------------------+
| statistical | p_trace           | Compute the Pillai's trace.                |
+-------------+-------------------+--------------------------------------------+
| statistical | range             | Compute the range (max - min) of each      |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | roy_root          | Compute the Roy's largest root.            |
+-------------+-------------------+--------------------------------------------+
| statistical | sd                | Compute the standard deviation of each     |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | sd_ratio          | Compute a statistical test for homogeneity |
|             |                   | of covariances.                            |
+-------------+-------------------+--------------------------------------------+
| statistical | skewness          | Compute the skewness for each attribute.   |
+-------------+-------------------+--------------------------------------------+
| statistical | sparsity          | Compute (possibly normalized) sparsity     |
|             |                   | metric for each attribute.                 |
+-------------+-------------------+--------------------------------------------+
| statistical | t_mean            | Compute the trimmed mean of each           |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | var               | Compute the variance of each attribute.    |
+-------------+-------------------+--------------------------------------------+
| statistical | w_lambda          | Compute the Wilks' Lambda value.           |
+-------------+-------------------+--------------------------------------------+

You can sort the metafeatures by name and group.

MFE.metafeature_description(sort_by_group=True, sort_by_mtf=True)
+-------------+-------------------+--------------------------------------------+
|    Group    | Meta-feature name |                Description                 |
+=============+===================+============================================+
| clustering  | ch                | Compute the Calinski and Harabasz index.   |
+-------------+-------------------+--------------------------------------------+
| clustering  | int               | Compute the INT index.                     |
+-------------+-------------------+--------------------------------------------+
| clustering  | nre               | Compute the normalized relative entropy.   |
+-------------+-------------------+--------------------------------------------+
| clustering  | pb                | Compute the pearson correlation between    |
|             |                   | class matching and instance distances.     |
+-------------+-------------------+--------------------------------------------+
| clustering  | sc                | Compute the number of clusters with size   |
|             |                   | smaller than a given size.                 |
+-------------+-------------------+--------------------------------------------+
| clustering  | sil               | Compute the mean silhouette value.         |
+-------------+-------------------+--------------------------------------------+
| clustering  | vdb               | Compute the Davies and Bouldin Index.      |
+-------------+-------------------+--------------------------------------------+
| clustering  | vdu               | Compute the Dunn Index.                    |
+-------------+-------------------+--------------------------------------------+
| complexity  | c1                | Compute the entropy of class proportions.  |
+-------------+-------------------+--------------------------------------------+
| complexity  | c2                | Compute the imbalance ratio.               |
+-------------+-------------------+--------------------------------------------+
| complexity  | cls_coef          | Clustering coefficient.                    |
+-------------+-------------------+--------------------------------------------+
| complexity  | density           | Average density of the network.            |
+-------------+-------------------+--------------------------------------------+
| complexity  | f1                | Maximum Fisher's discriminant ratio.       |
+-------------+-------------------+--------------------------------------------+
| complexity  | f1v               | Directional-vector maximum Fisher's        |
|             |                   | discriminant ratio.                        |
+-------------+-------------------+--------------------------------------------+
| complexity  | f2                | Volume of the overlapping region.          |
+-------------+-------------------+--------------------------------------------+
| complexity  | f3                | Compute feature maximum individual         |
|             |                   | efficiency.                                |
+-------------+-------------------+--------------------------------------------+
| complexity  | f4                | Compute the collective feature efficiency. |
+-------------+-------------------+--------------------------------------------+
| complexity  | hubs              | Hub score.                                 |
+-------------+-------------------+--------------------------------------------+
| complexity  | l1                | Sum of error distance by linear            |
|             |                   | programming.                               |
+-------------+-------------------+--------------------------------------------+
| complexity  | l2                | Compute the OVO subsets error rate of      |
|             |                   | linear classifier.                         |
+-------------+-------------------+--------------------------------------------+
| complexity  | l3                | Non-Linearity of a linear classifier.      |
+-------------+-------------------+--------------------------------------------+
| complexity  | lsc               | Local set average cardinality.             |
+-------------+-------------------+--------------------------------------------+
| complexity  | n1                | Compute the fraction of borderline points. |
+-------------+-------------------+--------------------------------------------+
| complexity  | n2                | Ratio of intra and extra class nearest     |
|             |                   | neighbor distance.                         |
+-------------+-------------------+--------------------------------------------+
| complexity  | n3                | Error rate of the nearest neighbor         |
|             |                   | classifier.                                |
+-------------+-------------------+--------------------------------------------+
| complexity  | n4                | Compute the non-linearity of the k-NN      |
|             |                   | Classifier.                                |
+-------------+-------------------+--------------------------------------------+
| complexity  | t1                | Fraction of hyperspheres covering data.    |
+-------------+-------------------+--------------------------------------------+
| complexity  | t2                | Compute the average number of features per |
|             |                   | dimension.                                 |
+-------------+-------------------+--------------------------------------------+
| complexity  | t3                | Compute the average number of PCA          |
|             |                   | dimensions per points.                     |
+-------------+-------------------+--------------------------------------------+
| complexity  | t4                | Compute the ratio of the PCA dimension to  |
|             |                   | the original dimension.                    |
+-------------+-------------------+--------------------------------------------+
| concept     | cohesiveness      | Compute the improved version of the        |
|             |                   | weighted distance, that captures how dense |
|             |                   | or sparse is the example distribution.     |
+-------------+-------------------+--------------------------------------------+
| concept     | conceptvar        | Compute the concept variation that         |
|             |                   | estimates the variability of class labels  |
|             |                   | among examples.                            |
+-------------+-------------------+--------------------------------------------+
| concept     | impconceptvar     | Compute the improved concept variation     |
|             |                   | that estimates the variability of class    |
|             |                   | labels among examples.                     |
+-------------+-------------------+--------------------------------------------+
| concept     | wg_dist           | Compute the weighted distance, that        |
|             |                   | captures how dense or sparse is the        |
|             |                   | example distribution.                      |
+-------------+-------------------+--------------------------------------------+
| general     | attr_to_inst      | Compute the ratio between the number of    |
|             |                   | attributes.                                |
+-------------+-------------------+--------------------------------------------+
| general     | cat_to_num        | Compute the ratio between the number of    |
|             |                   | categoric and numeric features.            |
+-------------+-------------------+--------------------------------------------+
| general     | freq_class        | Compute the relative frequency of each     |
|             |                   | distinct class.                            |
+-------------+-------------------+--------------------------------------------+
| general     | inst_to_attr      | Compute the ratio between the number of    |
|             |                   | instances and attributes.                  |
+-------------+-------------------+--------------------------------------------+
| general     | nr_attr           | Compute the total number of attributes.    |
+-------------+-------------------+--------------------------------------------+
| general     | nr_bin            | Compute the number of binary attributes.   |
+-------------+-------------------+--------------------------------------------+
| general     | nr_cat            | Compute the number of categorical          |
|             |                   | attributes.                                |
+-------------+-------------------+--------------------------------------------+
| general     | nr_class          | Compute the number of distinct classes.    |
+-------------+-------------------+--------------------------------------------+
| general     | nr_inst           | Compute the number of instances (rows) in  |
|             |                   | the dataset.                               |
+-------------+-------------------+--------------------------------------------+
| general     | nr_num            | Compute the number of numeric features.    |
+-------------+-------------------+--------------------------------------------+
| general     | num_to_cat        | Compute the number of numerical and        |
|             |                   | categorical features.                      |
+-------------+-------------------+--------------------------------------------+
| info-theory | attr_conc         | Compute concentration coef. of each pair   |
|             |                   | of distinct attributes.                    |
+-------------+-------------------+--------------------------------------------+
| info-theory | attr_ent          | Compute Shannon's entropy for each         |
|             |                   | predictive attribute.                      |
+-------------+-------------------+--------------------------------------------+
| info-theory | class_conc        | Compute concentration coefficient between  |
|             |                   | each attribute and class.                  |
+-------------+-------------------+--------------------------------------------+
| info-theory | class_ent         | Compute target attribute Shannon's         |
|             |                   | entropy.                                   |
+-------------+-------------------+--------------------------------------------+
| info-theory | eq_num_attr       | Compute the number of attributes           |
|             |                   | equivalent for a predictive task.          |
+-------------+-------------------+--------------------------------------------+
| info-theory | joint_ent         | Compute the joint entropy between each     |
|             |                   | attribute and class.                       |
+-------------+-------------------+--------------------------------------------+
| info-theory | mut_inf           | Compute the mutual information between     |
|             |                   | each attribute and target.                 |
+-------------+-------------------+--------------------------------------------+
| info-theory | ns_ratio          | Compute the noisiness of attributes.       |
+-------------+-------------------+--------------------------------------------+
| itemset     | one_itemset       | Compute the one itemset meta-feature.      |
+-------------+-------------------+--------------------------------------------+
| itemset     | two_itemset       | Compute the two itemset meta-feature.      |
+-------------+-------------------+--------------------------------------------+
| landmarking | best_node         | Performance of a the best single decision  |
|             |                   | tree node.                                 |
+-------------+-------------------+--------------------------------------------+
| landmarking | elite_nn          | Performance of Elite Nearest Neighbor.     |
+-------------+-------------------+--------------------------------------------+
| landmarking | linear_discr      | Performance of the Linear Discriminant     |
|             |                   | classifier.                                |
+-------------+-------------------+--------------------------------------------+
| landmarking | naive_bayes       | Performance of the Naive Bayes classifier. |
+-------------+-------------------+--------------------------------------------+
| landmarking | one_nn            | Performance of the 1-Nearest Neighbor      |
|             |                   | classifier.                                |
+-------------+-------------------+--------------------------------------------+
| landmarking | random_node       | Performance of the single decision tree    |
|             |                   | node model induced by a random attribute.  |
+-------------+-------------------+--------------------------------------------+
| landmarking | worst_node        | Performance of the single decision tree    |
|             |                   | node model induced by the worst            |
|             |                   | informative attribute.                     |
+-------------+-------------------+--------------------------------------------+
| model-based | leaves            | Compute the number of leaf nodes in the DT |
|             |                   | model.                                     |
+-------------+-------------------+--------------------------------------------+
| model-based | leaves_branch     | Compute the size of branches in the DT     |
|             |                   | model.                                     |
+-------------+-------------------+--------------------------------------------+
| model-based | leaves_corrob     | Compute the leaves corroboration of the DT |
|             |                   | model.                                     |
+-------------+-------------------+--------------------------------------------+
| model-based | leaves_homo       | Compute the DT model Homogeneity for every |
|             |                   | leaf node.                                 |
+-------------+-------------------+--------------------------------------------+
| model-based | leaves_per_class  | Compute the proportion of leaves per class |
|             |                   | in DT model.                               |
+-------------+-------------------+--------------------------------------------+
| model-based | nodes             | Compute the number of non-leaf nodes in DT |
|             |                   | model.                                     |
+-------------+-------------------+--------------------------------------------+
| model-based | nodes_per_attr    | Compute the ratio of nodes per number of   |
|             |                   | attributes in DT model.                    |
+-------------+-------------------+--------------------------------------------+
| model-based | nodes_per_inst    | Compute the ratio of non-leaf nodes per    |
|             |                   | number of instances in DT model.           |
+-------------+-------------------+--------------------------------------------+
| model-based | nodes_per_level   | Compute the ratio of number of nodes per   |
|             |                   | tree level in DT model.                    |
+-------------+-------------------+--------------------------------------------+
| model-based | nodes_repeated    | Compute the number of repeated nodes in DT |
|             |                   | model.                                     |
+-------------+-------------------+--------------------------------------------+
| model-based | tree_depth        | Compute the depth of every node in the DT  |
|             |                   | model.                                     |
+-------------+-------------------+--------------------------------------------+
| model-based | tree_imbalance    | Compute the tree imbalance for each leaf   |
|             |                   | node.                                      |
+-------------+-------------------+--------------------------------------------+
| model-based | tree_shape        | Compute the tree shape for every leaf      |
|             |                   | node.                                      |
+-------------+-------------------+--------------------------------------------+
| model-based | var_importance    | Compute the features importance of the DT  |
|             |                   | model for each attribute.                  |
+-------------+-------------------+--------------------------------------------+
| statistical | can_cor           | Compute canonical correlations of data.    |
+-------------+-------------------+--------------------------------------------+
| statistical | cor               | Compute the absolute value of the          |
|             |                   | correlation of distinct dataset column     |
|             |                   | pairs.                                     |
+-------------+-------------------+--------------------------------------------+
| statistical | cov               | Compute the absolute value of the          |
|             |                   | covariance of distinct dataset attribute   |
|             |                   | pairs.                                     |
+-------------+-------------------+--------------------------------------------+
| statistical | eigenvalues       | Compute the eigenvalues of covariance      |
|             |                   | matrix from dataset.                       |
+-------------+-------------------+--------------------------------------------+
| statistical | g_mean            | Compute the geometric mean of each         |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | gravity           | Compute the distance between minority and  |
|             |                   | majority classes center of mass.           |
+-------------+-------------------+--------------------------------------------+
| statistical | h_mean            | Compute the harmonic mean of each          |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | iq_range          | Compute the interquartile range (IQR) of   |
|             |                   | each attribute.                            |
+-------------+-------------------+--------------------------------------------+
| statistical | kurtosis          | Compute the kurtosis of each attribute.    |
+-------------+-------------------+--------------------------------------------+
| statistical | lh_trace          | Compute the Lawley-Hotelling trace.        |
+-------------+-------------------+--------------------------------------------+
| statistical | mad               | Compute the Median Absolute Deviation      |
|             |                   | (MAD) adjusted by a factor.                |
+-------------+-------------------+--------------------------------------------+
| statistical | max               | Compute the maximum value from each        |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | mean              | Compute the mean value of each attribute.  |
+-------------+-------------------+--------------------------------------------+
| statistical | median            | Compute the median value from each         |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | min               | Compute the minimum value from each        |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | nr_cor_attr       | Compute the number of distinct highly      |
|             |                   | correlated pair of attributes.             |
+-------------+-------------------+--------------------------------------------+
| statistical | nr_disc           | Compute the number of canonical            |
|             |                   | correlation between each attribute and     |
|             |                   | class.                                     |
+-------------+-------------------+--------------------------------------------+
| statistical | nr_norm           | Compute the number of attributes normally  |
|             |                   | distributed based in a given method.       |
+-------------+-------------------+--------------------------------------------+
| statistical | nr_outliers       | Compute the number of attributes with at   |
|             |                   | least one outlier value.                   |
+-------------+-------------------+--------------------------------------------+
| statistical | p_trace           | Compute the Pillai's trace.                |
+-------------+-------------------+--------------------------------------------+
| statistical | range             | Compute the range (max - min) of each      |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | roy_root          | Compute the Roy's largest root.            |
+-------------+-------------------+--------------------------------------------+
| statistical | sd                | Compute the standard deviation of each     |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | sd_ratio          | Compute a statistical test for homogeneity |
|             |                   | of covariances.                            |
+-------------+-------------------+--------------------------------------------+
| statistical | skewness          | Compute the skewness for each attribute.   |
+-------------+-------------------+--------------------------------------------+
| statistical | sparsity          | Compute (possibly normalized) sparsity     |
|             |                   | metric for each attribute.                 |
+-------------+-------------------+--------------------------------------------+
| statistical | t_mean            | Compute the trimmed mean of each           |
|             |                   | attribute.                                 |
+-------------+-------------------+--------------------------------------------+
| statistical | var               | Compute the variance of each attribute.    |
+-------------+-------------------+--------------------------------------------+
| statistical | w_lambda          | Compute the Wilks' Lambda value.           |
+-------------+-------------------+--------------------------------------------+

You can include the references.

MFE.metafeature_description(sort_by_group=True, sort_by_mtf=True,
                            include_references=True)
+-------------+-------------------+----------------------+---------------------+
|    Group    | Meta-feature name |     Description      |      Reference      |
+=============+===================+======================+=====================+
| clustering  | ch                | Compute the Calinski | [1] T. Calinski, J. |
|             |                   | and Harabasz index.  | Harabasz, A         |
|             |                   |                      | dendrite method for |
|             |                   |                      | cluster analysis,   |
|             |                   |                      | Commun. Stat.       |
|             |                   |                      | Theory Methods 3    |
|             |                   |                      | (1) (1974) 1–27.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| clustering  | int               | Compute the INT      | [1] SOUZA, Bruno    |
|             |                   | index.               | Feres de. Meta-     |
|             |                   |                      | aprendizagem        |
|             |                   |                      | aplicada à          |
|             |                   |                      | classificação de    |
|             |                   |                      | dados de expressão  |
|             |                   |                      | gênica. 2010. Tese  |
|             |                   |                      | (Doutorado em       |
|             |                   |                      | Ciências de         |
|             |                   |                      | Computação e        |
|             |                   |                      | Matemática          |
|             |                   |                      | Computacional),     |
|             |                   |                      | Instituto de        |
|             |                   |                      | Ciências            |
|             |                   |                      | Matemáticas e de    |
|             |                   |                      | Computação,         |
|             |                   |                      | Universidade de São |
|             |                   |                      | Paulo, São Carlos,  |
|             |                   |                      | 2010. doi:10.11606/ |
|             |                   |                      | T.55.2010.tde-04012 |
|             |                   |                      | 011-142551.         |
|             |                   |                      | [2] Bezdek, J. C.;  |
|             |                   |                      | Pal, N. R. (1998a). |
|             |                   |                      | Some new indexes of |
|             |                   |                      | cluster validity.   |
|             |                   |                      | IEEE Transactions   |
|             |                   |                      | on Systems, Man,    |
|             |                   |                      | and Cybernetics,    |
|             |                   |                      | Part B, v.28, n.3,  |
|             |                   |                      | p.301–315.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| clustering  | nre               | Compute the          | [1] Bruno Almeida   |
|             |                   | normalized relative  | Pimentel, André     |
|             |                   | entropy.             | C.P.L.F. de         |
|             |                   |                      | Carvalho. A new     |
|             |                   |                      | data                |
|             |                   |                      | characterization    |
|             |                   |                      | for selecting       |
|             |                   |                      | clustering          |
|             |                   |                      | algorithms using    |
|             |                   |                      | meta-learning.      |
|             |                   |                      | Information         |
|             |                   |                      | Sciences, Volume    |
|             |                   |                      | 477, 2019, Pages    |
|             |                   |                      | 203-219.            |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| clustering  | pb                | Compute the pearson  | [1] J. Lev, "The    |
|             |                   | correlation between  | Point Biserial      |
|             |                   | class matching and   | Coefficient of      |
|             |                   | instance distances.  | Correlation", Ann.  |
|             |                   |                      | Math. Statist.,     |
|             |                   |                      | Vol. 20, no.1, pp.  |
|             |                   |                      | 125-126, 1949.      |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| clustering  | sc                | Compute the number   | [1] Bruno Almeida   |
|             |                   | of clusters with     | Pimentel, André     |
|             |                   | size smaller than a  | C.P.L.F. de         |
|             |                   | given size.          | Carvalho. A new     |
|             |                   |                      | data                |
|             |                   |                      | characterization    |
|             |                   |                      | for selecting       |
|             |                   |                      | clustering          |
|             |                   |                      | algorithms using    |
|             |                   |                      | meta-learning.      |
|             |                   |                      | Information         |
|             |                   |                      | Sciences, Volume    |
|             |                   |                      | 477, 2019, Pages    |
|             |                   |                      | 203-219.            |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| clustering  | sil               | Compute the mean     | [1] P.J. Rousseeuw, |
|             |                   | silhouette value.    | Silhouettes: a      |
|             |                   |                      | graphical aid to    |
|             |                   |                      | the interpretation  |
|             |                   |                      | and validation of   |
|             |                   |                      | cluster analysis,   |
|             |                   |                      | J. Comput. Appl.    |
|             |                   |                      | Math. 20 (1987)     |
|             |                   |                      | 53–65.              |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| clustering  | vdb               | Compute the Davies   | [1] D.L. Davies,    |
|             |                   | and Bouldin Index.   | D.W. Bouldin, A     |
|             |                   |                      | cluster separation  |
|             |                   |                      | measure, IEEE       |
|             |                   |                      | Trans. Pattern      |
|             |                   |                      | Anal. Mach. Intell. |
|             |                   |                      | 1 (2) (1979)        |
|             |                   |                      | 224–227.            |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| clustering  | vdu               | Compute the Dunn     | [1] J.C. Dunn,      |
|             |                   | Index.               | Well-separated      |
|             |                   |                      | clusters and        |
|             |                   |                      | optimal fuzzy       |
|             |                   |                      | partitions, J.      |
|             |                   |                      | Cybern. 4 (1)       |
|             |                   |                      | (1974) 95–104.      |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | c1                | Compute the entropy  | [1] Ana C. Lorena,  |
|             |                   | of class             | Luís P. F. Garcia,  |
|             |                   | proportions.         | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 15). Published |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | c2                | Compute the          | [1] Ana C. Lorena,  |
|             |                   | imbalance ratio.     | Luís P. F. Garcia,  |
|             |                   |                      | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 16). Published |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | cls_coef          | Clustering           | [1] Ana C. Lorena,  |
|             |                   | coefficient.         | Luís P. F. Garcia,  |
|             |                   |                      | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | density           | Average density of   | [1] Ana C. Lorena,  |
|             |                   | the network.         | Luís P. F. Garcia,  |
|             |                   |                      | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | f1                | Maximum Fisher's     | [1] Ana C. Lorena,  |
|             |                   | discriminant ratio.  | Luís P. F. Garcia,  |
|             |                   |                      | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      | [2] Ramón A         |
|             |                   |                      | Mollineda, José S   |
|             |                   |                      | Sánchez, and José M |
|             |                   |                      | Sotoca. Data        |
|             |                   |                      | characterization    |
|             |                   |                      | for effective       |
|             |                   |                      | prototype           |
|             |                   |                      | selection. In 2nd   |
|             |                   |                      | Iberian Conference  |
|             |                   |                      | on Pattern          |
|             |                   |                      | Recognition and     |
|             |                   |                      | Image Analysis      |
|             |                   |                      | (IbPRIA), pages     |
|             |                   |                      | 27–34, 2005.        |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | f1v               | Directional-vector   | [1] Ana C. Lorena,  |
|             |                   | maximum Fisher's     | Luís P. F. Garcia,  |
|             |                   | discriminant ratio.  | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      | [2] Witold Malina.  |
|             |                   |                      | Two-parameter       |
|             |                   |                      | fisher criterion.   |
|             |                   |                      | IEEE Transactions   |
|             |                   |                      | on Systems, Man,    |
|             |                   |                      | and Cybernetics,    |
|             |                   |                      | Part B              |
|             |                   |                      | (Cybernetics),      |
|             |                   |                      | 31(4):629–636,      |
|             |                   |                      | 2001.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | f2                | Volume of the        | [1] Ana C. Lorena,  |
|             |                   | overlapping region.  | Luís P. F. Garcia,  |
|             |                   |                      | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      | [2] Marcilio C P    |
|             |                   |                      | Souto, Ana C        |
|             |                   |                      | Lorena, Newton      |
|             |                   |                      | Spolaôr, and Ivan G |
|             |                   |                      | Costa. Complexity   |
|             |                   |                      | measures of         |
|             |                   |                      | supervised          |
|             |                   |                      | classification      |
|             |                   |                      | tasks: a case study |
|             |                   |                      | for cancer gene     |
|             |                   |                      | expression data. In |
|             |                   |                      | International Joint |
|             |                   |                      | Conference on       |
|             |                   |                      | Neural Networks     |
|             |                   |                      | (IJCNN), pages      |
|             |                   |                      | 1352–1358, 2010.    |
|             |                   |                      | [3] Lisa Cummins.   |
|             |                   |                      | Combining and       |
|             |                   |                      | Choosing Case Base  |
|             |                   |                      | Maintenance         |
|             |                   |                      | Algorithms. PhD     |
|             |                   |                      | thesis, National    |
|             |                   |                      | University of       |
|             |                   |                      | Ireland, Cork,      |
|             |                   |                      | 2013.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | f3                | Compute feature      | [1] Ana C. Lorena,  |
|             |                   | maximum individual   | Luís P. F. Garcia,  |
|             |                   | efficiency.          | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 6). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | f4                | Compute the          | [1] Ana C. Lorena,  |
|             |                   | collective feature   | Luís P. F. Garcia,  |
|             |                   | efficiency.          | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 7). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | hubs              | Hub score.           | [1] Ana C. Lorena,  |
|             |                   |                      | Luís P. F. Garcia,  |
|             |                   |                      | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | l1                | Sum of error         | [1] Ana C. Lorena,  |
|             |                   | distance by linear   | Luís P. F. Garcia,  |
|             |                   | programming.         | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | l2                | Compute the OVO      | [1] Ana C. Lorena,  |
|             |                   | subsets error rate   | Luís P. F. Garcia,  |
|             |                   | of linear            | Jens Lehmann,       |
|             |                   | classifier.          | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | l3                | Non-Linearity of a   | [1] Ana C. Lorena,  |
|             |                   | linear classifier.   | Luís P. F. Garcia,  |
|             |                   |                      | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | lsc               | Local set average    | [1] Ana C. Lorena,  |
|             |                   | cardinality.         | Luís P. F. Garcia,  |
|             |                   |                      | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 15). Published |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      | [2] Enrique Leyva,  |
|             |                   |                      | Antonio González,   |
|             |                   |                      | and Raúl Pérez. A   |
|             |                   |                      | set of complexity   |
|             |                   |                      | measures designed   |
|             |                   |                      | for applying meta-  |
|             |                   |                      | learning to         |
|             |                   |                      | instance selection. |
|             |                   |                      | IEEE Transactions   |
|             |                   |                      | on Knowledge and    |
|             |                   |                      | Data Engineering,   |
|             |                   |                      | 27(2):354–367,      |
|             |                   |                      | 2014.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | n1                | Compute the fraction | [1] Ana C. Lorena,  |
|             |                   | of borderline        | Luís P. F. Garcia,  |
|             |                   | points.              | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9-10).         |
|             |                   |                      | Published in ACM    |
|             |                   |                      | Computing Surveys   |
|             |                   |                      | (CSUR), Volume 52   |
|             |                   |                      | Issue 5, October    |
|             |                   |                      | 2019, Article No.   |
|             |                   |                      | 107.                |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | n2                | Ratio of intra and   | [1] Ana C. Lorena,  |
|             |                   | extra class nearest  | Luís P. F. Garcia,  |
|             |                   | neighbor distance.   | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | n3                | Error rate of the    | [1] Ana C. Lorena,  |
|             |                   | nearest neighbor     | Luís P. F. Garcia,  |
|             |                   | classifier.          | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | n4                | Compute the non-     | [1] Ana C. Lorena,  |
|             |                   | linearity of the     | Luís P. F. Garcia,  |
|             |                   | k-NN Classifier.     | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9-11).         |
|             |                   |                      | Published in ACM    |
|             |                   |                      | Computing Surveys   |
|             |                   |                      | (CSUR), Volume 52   |
|             |                   |                      | Issue 5, October    |
|             |                   |                      | 2019, Article No.   |
|             |                   |                      | 107.                |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | t1                | Fraction of          | [1] Ana C. Lorena,  |
|             |                   | hyperspheres         | Luís P. F. Garcia,  |
|             |                   | covering data.       | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 9). Published  |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      | [2] Tin K Ho and    |
|             |                   |                      | Mitra Basu.         |
|             |                   |                      | Complexity measures |
|             |                   |                      | of supervised       |
|             |                   |                      | classification      |
|             |                   |                      | problems. IEEE      |
|             |                   |                      | Transactions on     |
|             |                   |                      | Pattern Analysis    |
|             |                   |                      | and Machine         |
|             |                   |                      | Intelligence,       |
|             |                   |                      | 24(3):289–300,      |
|             |                   |                      | 2002.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | t2                | Compute the average  | [1] Ana C. Lorena,  |
|             |                   | number of features   | Luís P. F. Garcia,  |
|             |                   | per dimension.       | Jens Lehmann,       |
|             |                   |                      | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 15). Published |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | t3                | Compute the average  | [1] Ana C. Lorena,  |
|             |                   | number of PCA        | Luís P. F. Garcia,  |
|             |                   | dimensions per       | Jens Lehmann,       |
|             |                   | points.              | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 15). Published |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| complexity  | t4                | Compute the ratio of | [1] Ana C. Lorena,  |
|             |                   | the PCA dimension to | Luís P. F. Garcia,  |
|             |                   | the original         | Jens Lehmann,       |
|             |                   | dimension.           | Marcilio C. P.      |
|             |                   |                      | Souto, and Tin K.   |
|             |                   |                      | Ho. How Complex is  |
|             |                   |                      | your classification |
|             |                   |                      | problem? A survey   |
|             |                   |                      | on measuring        |
|             |                   |                      | classification      |
|             |                   |                      | complexity (V2).    |
|             |                   |                      | (2019) (Cited on    |
|             |                   |                      | page 15). Published |
|             |                   |                      | in ACM Computing    |
|             |                   |                      | Surveys (CSUR),     |
|             |                   |                      | Volume 52 Issue 5,  |
|             |                   |                      | October 2019,       |
|             |                   |                      | Article No. 107.    |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| concept     | cohesiveness      | Compute the improved | [1] Vilalta, R and  |
|             |                   | version of the       | Drissi, Y (2002). A |
|             |                   | weighted distance,   | Characterization of |
|             |                   | that captures how    | Difficult Problems  |
|             |                   | dense or sparse is   | in Classification.  |
|             |                   | the example          | Proceedings of the  |
|             |                   | distribution.        | 2002 International  |
|             |                   |                      | Conference on       |
|             |                   |                      | Machine Learning    |
|             |                   |                      | and Applications    |
|             |                   |                      | (pp. 133-138).      |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| concept     | conceptvar        | Compute the concept  | [1] Vilalta, R.     |
|             |                   | variation that       | (1999).             |
|             |                   | estimates the        | Understanding       |
|             |                   | variability of class | accuracy            |
|             |                   | labels among         | performance through |
|             |                   | examples.            | concept             |
|             |                   |                      | characterization    |
|             |                   |                      | and algorithm       |
|             |                   |                      | analysis. In        |
|             |                   |                      | Proceedings of the  |
|             |                   |                      | ICML-99 workshop on |
|             |                   |                      | recent advances in  |
|             |                   |                      | meta-learning and   |
|             |                   |                      | future work (pp.    |
|             |                   |                      | 3-9).               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| concept     | impconceptvar     | Compute the improved | [1] Vilalta, R and  |
|             |                   | concept variation    | Drissi, Y (2002). A |
|             |                   | that estimates the   | Characterization of |
|             |                   | variability of class | Difficult Problems  |
|             |                   | labels among         | in Classification.  |
|             |                   | examples.            | Proceedings of the  |
|             |                   |                      | 2002 International  |
|             |                   |                      | Conference on       |
|             |                   |                      | Machine Learning    |
|             |                   |                      | and Applications    |
|             |                   |                      | (pp. 133-138).      |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| concept     | wg_dist           | Compute the weighted | [1] Vilalta, R.     |
|             |                   | distance, that       | (1999).             |
|             |                   | captures how dense   | Understanding       |
|             |                   | or sparse is the     | accuracy            |
|             |                   | example              | performance through |
|             |                   | distribution.        | concept             |
|             |                   |                      | characterization    |
|             |                   |                      | and algorithm       |
|             |                   |                      | analysis. In        |
|             |                   |                      | Proceedings of the  |
|             |                   |                      | ICML-99 workshop on |
|             |                   |                      | recent advances in  |
|             |                   |                      | meta-learning and   |
|             |                   |                      | future work (pp.    |
|             |                   |                      | 3-9).               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| general     | attr_to_inst      | Compute the ratio    | [1] Alexandros      |
|             |                   | between the number   | Kalousis and        |
|             |                   | of attributes.       | Theoharis           |
|             |                   |                      | Theoharis. NOEMON:  |
|             |                   |                      | Design,             |
|             |                   |                      | implementation and  |
|             |                   |                      | performance results |
|             |                   |                      | of an intelligent   |
|             |                   |                      | assistant for       |
|             |                   |                      | classifier          |
|             |                   |                      | selection.          |
|             |                   |                      | Intelligent Data    |
|             |                   |                      | Analysis,           |
|             |                   |                      | 3(5):319–337, 1999. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| general     | cat_to_num        | Compute the ratio    | [1] Matthias        |
|             |                   | between the number   | Feurer, Jost Tobias |
|             |                   | of categoric and     | Springenberg, and   |
|             |                   | numeric features.    | Frank Hutter. Using |
|             |                   |                      | meta-learning       |
|             |                   |                      | toinitialize        |
|             |                   |                      | bayesian            |
|             |                   |                      | optimization of     |
|             |                   |                      | hyperparameters. In |
|             |                   |                      | International       |
|             |                   |                      | Conference on Meta- |
|             |                   |                      | learning and        |
|             |                   |                      | Algorithm Selection |
|             |                   |                      | (MLAS), pages 3 –   |
|             |                   |                      | 10, 2014.           |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| general     | freq_class        | Compute the relative | [1] Guido Lindner   |
|             |                   | frequency of each    | and Rudi Studer.    |
|             |                   | distinct class.      | AST: Support for    |
|             |                   |                      | algorithm selection |
|             |                   |                      | with a CBR          |
|             |                   |                      | approach. In        |
|             |                   |                      | European Conference |
|             |                   |                      | on Principles of    |
|             |                   |                      | Data Mining and     |
|             |                   |                      | Knowledge Discovery |
|             |                   |                      | (PKDD), pages 418 – |
|             |                   |                      | 423, 1999.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| general     | inst_to_attr      | Compute the ratio    | [1] Petr Kuba,      |
|             |                   | between the number   | Pavel Brazdil,      |
|             |                   | of instances and     | Carlos Soares, and  |
|             |                   | attributes.          | Adam Woznica.       |
|             |                   |                      | Exploiting sampling |
|             |                   |                      | andmeta-learning    |
|             |                   |                      | for parameter       |
|             |                   |                      | setting for support |
|             |                   |                      | vector machines. In |
|             |                   |                      | 8th IBERAMIA        |
|             |                   |                      | Workshop on         |
|             |                   |                      | Learning and Data   |
|             |                   |                      | Mining, pages 209 – |
|             |                   |                      | 216, 2002.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| general     | nr_attr           | Compute the total    | [1] Donald Michie,  |
|             |                   | number of            | David J.            |
|             |                   | attributes.          | Spiegelhalter,      |
|             |                   |                      | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| general     | nr_bin            | Compute the number   | [1] Donald Michie,  |
|             |                   | of binary            | David J.            |
|             |                   | attributes.          | Spiegelhalter,      |
|             |                   |                      | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| general     | nr_cat            | Compute the number   | [1] Robert Engels   |
|             |                   | of categorical       | and Christiane      |
|             |                   | attributes.          | Theusinger. Using a |
|             |                   |                      | data metric for     |
|             |                   |                      | preprocessing       |
|             |                   |                      | advice for data     |
|             |                   |                      | mining              |
|             |                   |                      | applications. In    |
|             |                   |                      | 13th European       |
|             |                   |                      | Conference on on    |
|             |                   |                      | Artificial          |
|             |                   |                      | Intelligence        |
|             |                   |                      | (ECAI), pages 430 – |
|             |                   |                      | 434, 1998.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| general     | nr_class          | Compute the number   | [1] Donald Michie,  |
|             |                   | of distinct classes. | David J.            |
|             |                   |                      | Spiegelhalter,      |
|             |                   |                      | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| general     | nr_inst           | Compute the number   | [1] Donald Michie,  |
|             |                   | of instances (rows)  | David J.            |
|             |                   | in the dataset.      | Spiegelhalter,      |
|             |                   |                      | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| general     | nr_num            | Compute the number   | [1] Robert Engels   |
|             |                   | of numeric features. | and Christiane      |
|             |                   |                      | Theusinger. Using a |
|             |                   |                      | data metric for     |
|             |                   |                      | preprocessing       |
|             |                   |                      | advice for data     |
|             |                   |                      | mining              |
|             |                   |                      | applications. In    |
|             |                   |                      | 13th European       |
|             |                   |                      | Conference on on    |
|             |                   |                      | Artificial          |
|             |                   |                      | Intelligence        |
|             |                   |                      | (ECAI), pages 430 – |
|             |                   |                      | 434, 1998.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| general     | num_to_cat        | Compute the number   | [1] Matthias        |
|             |                   | of numerical and     | Feurer, Jost Tobias |
|             |                   | categorical          | Springenberg, and   |
|             |                   | features.            | Frank Hutter. Using |
|             |                   |                      | meta-learning       |
|             |                   |                      | toinitialize        |
|             |                   |                      | bayesian            |
|             |                   |                      | optimization of     |
|             |                   |                      | hyperparameters. In |
|             |                   |                      | International       |
|             |                   |                      | Conference on Meta- |
|             |                   |                      | learning and        |
|             |                   |                      | Algorithm Selection |
|             |                   |                      | (MLAS), pages 3 –   |
|             |                   |                      | 10, 2014.           |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| info-theory | attr_conc         | Compute              | [1] Alexandros      |
|             |                   | concentration coef.  | Kalousis and        |
|             |                   | of each pair of      | Melanie Hilario.    |
|             |                   | distinct attributes. | Model selection via |
|             |                   |                      | meta-learning: a    |
|             |                   |                      | comparative study.  |
|             |                   |                      | International       |
|             |                   |                      | Journal on          |
|             |                   |                      | Artificial          |
|             |                   |                      | Intelligence Tools, |
|             |                   |                      | 10(4):525–554,      |
|             |                   |                      | 2001.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| info-theory | attr_ent          | Compute Shannon's    | [1] Donald Michie,  |
|             |                   | entropy for each     | David J.            |
|             |                   | predictive           | Spiegelhalter,      |
|             |                   | attribute.           | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| info-theory | class_conc        | Compute              | [1] Alexandros      |
|             |                   | concentration        | Kalousis and        |
|             |                   | coefficient between  | Melanie Hilario.    |
|             |                   | each attribute and   | Model selection via |
|             |                   | class.               | meta-learning: a    |
|             |                   |                      | comparative study.  |
|             |                   |                      | International       |
|             |                   |                      | Journal on          |
|             |                   |                      | Artificial          |
|             |                   |                      | Intelligence Tools, |
|             |                   |                      | 10(4):525–554,      |
|             |                   |                      | 2001.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| info-theory | class_ent         | Compute target       | [1] Donald Michie,  |
|             |                   | attribute Shannon's  | David J.            |
|             |                   | entropy.             | Spiegelhalter,      |
|             |                   |                      | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| info-theory | eq_num_attr       | Compute the number   | [1] Donald Michie,  |
|             |                   | of attributes        | David J.            |
|             |                   | equivalent for a     | Spiegelhalter,      |
|             |                   | predictive task.     | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| info-theory | joint_ent         | Compute the joint    | [1] Donald Michie,  |
|             |                   | entropy between each | David J.            |
|             |                   | attribute and class. | Spiegelhalter,      |
|             |                   |                      | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| info-theory | mut_inf           | Compute the mutual   | [1] Donald Michie,  |
|             |                   | information between  | David J.            |
|             |                   | each attribute and   | Spiegelhalter,      |
|             |                   | target.              | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| info-theory | ns_ratio          | Compute the          | [1] Donald Michie,  |
|             |                   | noisiness of         | David J.            |
|             |                   | attributes.          | Spiegelhalter,      |
|             |                   |                      | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| itemset     | one_itemset       | Compute the one      | [1] Song, Q., Wang, |
|             |                   | itemset meta-        | G., & Wang, C.      |
|             |                   | feature.             | (2012). Automatic   |
|             |                   |                      | recommendation of   |
|             |                   |                      | classification      |
|             |                   |                      | algorithms based on |
|             |                   |                      | data set            |
|             |                   |                      | characteristics.    |
|             |                   |                      | Pattern             |
|             |                   |                      | recognition, 45(7), |
|             |                   |                      | 2672-2689.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| itemset     | two_itemset       | Compute the two      | [1] Song, Q., Wang, |
|             |                   | itemset meta-        | G., & Wang, C.      |
|             |                   | feature.             | (2012). Automatic   |
|             |                   |                      | recommendation of   |
|             |                   |                      | classification      |
|             |                   |                      | algorithms based on |
|             |                   |                      | data set            |
|             |                   |                      | characteristics.    |
|             |                   |                      | Pattern             |
|             |                   |                      | recognition, 45(7), |
|             |                   |                      | 2672-2689.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| landmarking | best_node         | Performance of a the | [1] Hilan Bensusan  |
|             |                   | best single decision | and Christophe      |
|             |                   | tree node.           | Giraud-Carrier.     |
|             |                   |                      | Discovering task    |
|             |                   |                      | neighbourhoods      |
|             |                   |                      | through landmark    |
|             |                   |                      | learning            |
|             |                   |                      | performances. In    |
|             |                   |                      | 4th European        |
|             |                   |                      | Conference on       |
|             |                   |                      | Principles of Data  |
|             |                   |                      | Mining and          |
|             |                   |                      | Knowledge Discovery |
|             |                   |                      | (PKDD), pages 325 – |
|             |                   |                      | 330, 2000.          |
|             |                   |                      | [2] Johannes        |
|             |                   |                      | Furnkranz and       |
|             |                   |                      | Johann Petrak. An   |
|             |                   |                      | evaluation of       |
|             |                   |                      | landmarking         |
|             |                   |                      | variants. In 1st    |
|             |                   |                      | ECML/PKDD           |
|             |                   |                      | International       |
|             |                   |                      | Workshop on         |
|             |                   |                      | Integration and     |
|             |                   |                      | Collaboration       |
|             |                   |                      | Aspects of Data     |
|             |                   |                      | Mining, Decision    |
|             |                   |                      | Support and Meta-   |
|             |                   |                      | Learning (IDDM),    |
|             |                   |                      | pages 57 – 68,      |
|             |                   |                      | 2001.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| landmarking | elite_nn          | Performance of Elite | [1] Hilan Bensusan  |
|             |                   | Nearest Neighbor.    | and Christophe      |
|             |                   |                      | Giraud-Carrier.     |
|             |                   |                      | Discovering task    |
|             |                   |                      | neighbourhoods      |
|             |                   |                      | through landmark    |
|             |                   |                      | learning            |
|             |                   |                      | performances. In    |
|             |                   |                      | 4th European        |
|             |                   |                      | Conference on       |
|             |                   |                      | Principles of Data  |
|             |                   |                      | Mining and          |
|             |                   |                      | Knowledge Discovery |
|             |                   |                      | (PKDD), pages 325 – |
|             |                   |                      | 330, 2000.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| landmarking | linear_discr      | Performance of the   | [1] Hilan Bensusan  |
|             |                   | Linear Discriminant  | and Christophe      |
|             |                   | classifier.          | Giraud-Carrier.     |
|             |                   |                      | Discovering task    |
|             |                   |                      | neighbourhoods      |
|             |                   |                      | through landmark    |
|             |                   |                      | learning            |
|             |                   |                      | performances. In    |
|             |                   |                      | 4th European        |
|             |                   |                      | Conference on       |
|             |                   |                      | Principles of Data  |
|             |                   |                      | Mining and          |
|             |                   |                      | Knowledge Discovery |
|             |                   |                      | (PKDD), pages 325 – |
|             |                   |                      | 330, 2000.          |
|             |                   |                      | [2] Johannes        |
|             |                   |                      | Furnkranz and       |
|             |                   |                      | Johann Petrak. An   |
|             |                   |                      | evaluation of       |
|             |                   |                      | landmarking         |
|             |                   |                      | variants. In 1st    |
|             |                   |                      | ECML/PKDD           |
|             |                   |                      | International       |
|             |                   |                      | Workshop on         |
|             |                   |                      | Integration and     |
|             |                   |                      | Collaboration       |
|             |                   |                      | Aspects of Data     |
|             |                   |                      | Mining, Decision    |
|             |                   |                      | Support and Meta-   |
|             |                   |                      | Learning (IDDM),    |
|             |                   |                      | pages 57 – 68,      |
|             |                   |                      | 2001.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| landmarking | naive_bayes       | Performance of the   | [1] Hilan Bensusan  |
|             |                   | Naive Bayes          | and Christophe      |
|             |                   | classifier.          | Giraud-Carrier.     |
|             |                   |                      | Discovering task    |
|             |                   |                      | neighbourhoods      |
|             |                   |                      | through landmark    |
|             |                   |                      | learning            |
|             |                   |                      | performances. In    |
|             |                   |                      | 4th European        |
|             |                   |                      | Conference on       |
|             |                   |                      | Principles of Data  |
|             |                   |                      | Mining and          |
|             |                   |                      | Knowledge Discovery |
|             |                   |                      | (PKDD), pages 325 – |
|             |                   |                      | 330, 2000.          |
|             |                   |                      | [2] Johannes        |
|             |                   |                      | Furnkranz and       |
|             |                   |                      | Johann Petrak. An   |
|             |                   |                      | evaluation of       |
|             |                   |                      | landmarking         |
|             |                   |                      | variants. In 1st    |
|             |                   |                      | ECML/PKDD           |
|             |                   |                      | International       |
|             |                   |                      | Workshop on         |
|             |                   |                      | Integration and     |
|             |                   |                      | Collaboration       |
|             |                   |                      | Aspects of Data     |
|             |                   |                      | Mining, Decision    |
|             |                   |                      | Support and Meta-   |
|             |                   |                      | Learning (IDDM),    |
|             |                   |                      | pages 57 – 68,      |
|             |                   |                      | 2001.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| landmarking | one_nn            | Performance of the   | [1] Hilan Bensusan  |
|             |                   | 1-Nearest Neighbor   | and Christophe      |
|             |                   | classifier.          | Giraud-Carrier.     |
|             |                   |                      | Discovering task    |
|             |                   |                      | neighbourhoods      |
|             |                   |                      | through landmark    |
|             |                   |                      | learning            |
|             |                   |                      | performances. In    |
|             |                   |                      | 4th European        |
|             |                   |                      | Conference on       |
|             |                   |                      | Principles of Data  |
|             |                   |                      | Mining and          |
|             |                   |                      | Knowledge Discovery |
|             |                   |                      | (PKDD), pages 325 – |
|             |                   |                      | 330, 2000.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| landmarking | random_node       | Performance of the   | [1] Hilan Bensusan  |
|             |                   | single decision tree | and Christophe      |
|             |                   | node model induced   | Giraud-Carrier.     |
|             |                   | by a random          | Discovering task    |
|             |                   | attribute.           | neighbourhoods      |
|             |                   |                      | through landmark    |
|             |                   |                      | learning            |
|             |                   |                      | performances. In    |
|             |                   |                      | 4th European        |
|             |                   |                      | Conference on       |
|             |                   |                      | Principles of Data  |
|             |                   |                      | Mining and          |
|             |                   |                      | Knowledge Discovery |
|             |                   |                      | (PKDD), pages 325 – |
|             |                   |                      | 330, 2000.          |
|             |                   |                      | [2] Johannes        |
|             |                   |                      | Furnkranz and       |
|             |                   |                      | Johann Petrak. An   |
|             |                   |                      | evaluation of       |
|             |                   |                      | landmarking         |
|             |                   |                      | variants. In 1st    |
|             |                   |                      | ECML/PKDD           |
|             |                   |                      | International       |
|             |                   |                      | Workshop on         |
|             |                   |                      | Integration and     |
|             |                   |                      | Collaboration       |
|             |                   |                      | Aspects of Data     |
|             |                   |                      | Mining, Decision    |
|             |                   |                      | Support and Meta-   |
|             |                   |                      | Learning (IDDM),    |
|             |                   |                      | pages 57 – 68,      |
|             |                   |                      | 2001.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| landmarking | worst_node        | Performance of the   | [1] Hilan Bensusan  |
|             |                   | single decision tree | and Christophe      |
|             |                   | node model induced   | Giraud-Carrier.     |
|             |                   | by the worst         | Discovering task    |
|             |                   | informative          | neighbourhoods      |
|             |                   | attribute.           | through landmark    |
|             |                   |                      | learning            |
|             |                   |                      | performances. In    |
|             |                   |                      | 4th European        |
|             |                   |                      | Conference on       |
|             |                   |                      | Principles of Data  |
|             |                   |                      | Mining and          |
|             |                   |                      | Knowledge Discovery |
|             |                   |                      | (PKDD), pages 325 – |
|             |                   |                      | 330, 2000.          |
|             |                   |                      | [2] Johannes        |
|             |                   |                      | Furnkranz and       |
|             |                   |                      | Johann Petrak. An   |
|             |                   |                      | evaluation of       |
|             |                   |                      | landmarking         |
|             |                   |                      | variants. In 1st    |
|             |                   |                      | ECML/PKDD           |
|             |                   |                      | International       |
|             |                   |                      | Workshop on         |
|             |                   |                      | Integration and     |
|             |                   |                      | Collaboration       |
|             |                   |                      | Aspects of Data     |
|             |                   |                      | Mining, Decision    |
|             |                   |                      | Support and Meta-   |
|             |                   |                      | Learning (IDDM),    |
|             |                   |                      | pages 57 – 68,      |
|             |                   |                      | 2001.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | leaves            | Compute the number   | [1] Yonghong Peng,  |
|             |                   | of leaf nodes in the | PA Flach, Pavel     |
|             |                   | DT model.            | Brazdil, and Carlos |
|             |                   |                      | Soares. Decision    |
|             |                   |                      | tree-based data     |
|             |                   |                      | characterization    |
|             |                   |                      | for meta-learning.  |
|             |                   |                      | In 2nd ECML/PKDD    |
|             |                   |                      | International       |
|             |                   |                      | Workshop on         |
|             |                   |                      | Integration and     |
|             |                   |                      | Collaboration       |
|             |                   |                      | Aspects of Data     |
|             |                   |                      | Mining, Decision    |
|             |                   |                      | Support and Meta-   |
|             |                   |                      | Learning(IDDM),     |
|             |                   |                      | pages 111 – 122,    |
|             |                   |                      | 2002a.              |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | leaves_branch     | Compute the size of  | [1] Yonghong Peng,  |
|             |                   | branches in the DT   | PA Flach, Pavel     |
|             |                   | model.               | Brazdil, and Carlos |
|             |                   |                      | Soares. Decision    |
|             |                   |                      | tree-based data     |
|             |                   |                      | characterization    |
|             |                   |                      | for meta-learning.  |
|             |                   |                      | In 2nd ECML/PKDD    |
|             |                   |                      | International       |
|             |                   |                      | Workshop on         |
|             |                   |                      | Integration and     |
|             |                   |                      | Collaboration       |
|             |                   |                      | Aspects of Data     |
|             |                   |                      | Mining, Decision    |
|             |                   |                      | Support and Meta-   |
|             |                   |                      | Learning(IDDM),     |
|             |                   |                      | pages 111 – 122,    |
|             |                   |                      | 2002a.              |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | leaves_corrob     | Compute the leaves   | [1] Hilan Bensusan, |
|             |                   | corroboration of the | Christophe Giraud-  |
|             |                   | DT model.            | Carrier, and Claire |
|             |                   |                      | Kennedy. A higher-  |
|             |                   |                      | order approachto    |
|             |                   |                      | meta-learning. In   |
|             |                   |                      | 10th International  |
|             |                   |                      | Conference          |
|             |                   |                      | Inductive Logic     |
|             |                   |                      | Programming (ILP),  |
|             |                   |                      | pages 33 – 42,      |
|             |                   |                      | 2000.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | leaves_homo       | Compute the DT model | [1] Hilan Bensusan, |
|             |                   | Homogeneity for      | Christophe Giraud-  |
|             |                   | every leaf node.     | Carrier, and Claire |
|             |                   |                      | Kennedy. A higher-  |
|             |                   |                      | order approachto    |
|             |                   |                      | meta-learning. In   |
|             |                   |                      | 10th International  |
|             |                   |                      | Conference          |
|             |                   |                      | Inductive Logic     |
|             |                   |                      | Programming (ILP),  |
|             |                   |                      | pages 33 – 42,      |
|             |                   |                      | 2000.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | leaves_per_class  | Compute the          | [1] Andray          |
|             |                   | proportion of leaves | Filchenkov and      |
|             |                   | per class in DT      | Arseniy Pendryak.   |
|             |                   | model.               | Datasets meta-      |
|             |                   |                      | feature description |
|             |                   |                      | for recom-mending   |
|             |                   |                      | feature selection   |
|             |                   |                      | algorithm. In       |
|             |                   |                      | Artificial          |
|             |                   |                      | Intelligence and    |
|             |                   |                      | Natural Language    |
|             |                   |                      | and Information     |
|             |                   |                      | Extraction, Social  |
|             |                   |                      | Media and Web       |
|             |                   |                      | Search FRUCT        |
|             |                   |                      | Conference (AINL-   |
|             |                   |                      | ISMWFRUCT), pages   |
|             |                   |                      | 11 – 18, 2015.      |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | nodes             | Compute the number   | [1] Yonghong Peng,  |
|             |                   | of non-leaf nodes in | PA Flach, Pavel     |
|             |                   | DT model.            | Brazdil, and Carlos |
|             |                   |                      | Soares. Decision    |
|             |                   |                      | tree-based data     |
|             |                   |                      | characterization    |
|             |                   |                      | for meta-learning.  |
|             |                   |                      | In 2nd ECML/PKDD    |
|             |                   |                      | International       |
|             |                   |                      | Workshop on         |
|             |                   |                      | Integration and     |
|             |                   |                      | Collaboration       |
|             |                   |                      | Aspects of Data     |
|             |                   |                      | Mining, Decision    |
|             |                   |                      | Support and Meta-   |
|             |                   |                      | Learning(IDDM),     |
|             |                   |                      | pages 111 – 122,    |
|             |                   |                      | 2002a.              |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | nodes_per_attr    | Compute the ratio of | [1] Hilan Bensusan, |
|             |                   | nodes per number of  | Christophe Giraud-  |
|             |                   | attributes in DT     | Carrier, and Claire |
|             |                   | model.               | Kennedy. A higher-  |
|             |                   |                      | order approachto    |
|             |                   |                      | meta-learning. In   |
|             |                   |                      | 10th International  |
|             |                   |                      | Conference          |
|             |                   |                      | Inductive Logic     |
|             |                   |                      | Programming (ILP),  |
|             |                   |                      | pages 33 – 42,      |
|             |                   |                      | 2000.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | nodes_per_inst    | Compute the ratio of | [1] Hilan Bensusan, |
|             |                   | non-leaf nodes per   | Christophe Giraud-  |
|             |                   | number of instances  | Carrier, and Claire |
|             |                   | in DT model.         | Kennedy. A higher-  |
|             |                   |                      | order approachto    |
|             |                   |                      | meta-learning. In   |
|             |                   |                      | 10th International  |
|             |                   |                      | Conference          |
|             |                   |                      | Inductive Logic     |
|             |                   |                      | Programming (ILP),  |
|             |                   |                      | pages 33 – 42,      |
|             |                   |                      | 2000.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | nodes_per_level   | Compute the ratio of | [1] Yonghong Peng,  |
|             |                   | number of nodes per  | PA Flach, Pavel     |
|             |                   | tree level in DT     | Brazdil, and Carlos |
|             |                   | model.               | Soares. Decision    |
|             |                   |                      | tree-based data     |
|             |                   |                      | characterization    |
|             |                   |                      | for meta-learning.  |
|             |                   |                      | In 2nd ECML/PKDD    |
|             |                   |                      | International       |
|             |                   |                      | Workshop on         |
|             |                   |                      | Integration and     |
|             |                   |                      | Collaboration       |
|             |                   |                      | Aspects of Data     |
|             |                   |                      | Mining, Decision    |
|             |                   |                      | Support and Meta-   |
|             |                   |                      | Learning(IDDM),     |
|             |                   |                      | pages 111 – 122,    |
|             |                   |                      | 2002a.              |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | nodes_repeated    | Compute the number   | [1] Hilan Bensusan, |
|             |                   | of repeated nodes in | Christophe Giraud-  |
|             |                   | DT model.            | Carrier, and Claire |
|             |                   |                      | Kennedy. A higher-  |
|             |                   |                      | order approachto    |
|             |                   |                      | meta-learning. In   |
|             |                   |                      | 10th International  |
|             |                   |                      | Conference          |
|             |                   |                      | Inductive Logic     |
|             |                   |                      | Programming (ILP),  |
|             |                   |                      | pages 33 – 42,      |
|             |                   |                      | 2000.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | tree_depth        | Compute the depth of | [1] Yonghong Peng,  |
|             |                   | every node in the DT | PA Flach, Pavel     |
|             |                   | model.               | Brazdil, and Carlos |
|             |                   |                      | Soares. Decision    |
|             |                   |                      | tree-based data     |
|             |                   |                      | characterization    |
|             |                   |                      | for meta-learning.  |
|             |                   |                      | In 2nd ECML/PKDD    |
|             |                   |                      | International       |
|             |                   |                      | Workshop on         |
|             |                   |                      | Integration and     |
|             |                   |                      | Collaboration       |
|             |                   |                      | Aspects of Data     |
|             |                   |                      | Mining, Decision    |
|             |                   |                      | Support and Meta-   |
|             |                   |                      | Learning(IDDM),     |
|             |                   |                      | pages 111 – 122,    |
|             |                   |                      | 2002a.              |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | tree_imbalance    | Compute the tree     | [1] Hilan Bensusan, |
|             |                   | imbalance for each   | Christophe Giraud-  |
|             |                   | leaf node.           | Carrier, and Claire |
|             |                   |                      | Kennedy. A higher-  |
|             |                   |                      | order approachto    |
|             |                   |                      | meta-learning. In   |
|             |                   |                      | 10th International  |
|             |                   |                      | Conference          |
|             |                   |                      | Inductive Logic     |
|             |                   |                      | Programming (ILP),  |
|             |                   |                      | pages 33 – 42,      |
|             |                   |                      | 2000.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | tree_shape        | Compute the tree     | [1] Hilan Bensusan, |
|             |                   | shape for every leaf | Christophe Giraud-  |
|             |                   | node.                | Carrier, and Claire |
|             |                   |                      | Kennedy. A higher-  |
|             |                   |                      | order approachto    |
|             |                   |                      | meta-learning. In   |
|             |                   |                      | 10th International  |
|             |                   |                      | Conference          |
|             |                   |                      | Inductive Logic     |
|             |                   |                      | Programming (ILP),  |
|             |                   |                      | pages 33 – 42,      |
|             |                   |                      | 2000.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| model-based | var_importance    | Compute the features | [1] Hilan Bensusan, |
|             |                   | importance of the DT | Christophe Giraud-  |
|             |                   | model for each       | Carrier, and Claire |
|             |                   | attribute.           | Kennedy. A higher-  |
|             |                   |                      | order approachto    |
|             |                   |                      | meta-learning. In   |
|             |                   |                      | 10th International  |
|             |                   |                      | Conference          |
|             |                   |                      | Inductive Logic     |
|             |                   |                      | Programming (ILP),  |
|             |                   |                      | pages 33 – 42,      |
|             |                   |                      | 2000.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | can_cor           | Compute canonical    | [1] Alexandros      |
|             |                   | correlations of      | Kalousis. Algorithm |
|             |                   | data.                | Selection via Meta- |
|             |                   |                      | Learning. PhD       |
|             |                   |                      | thesis, Faculty of  |
|             |                   |                      | Science of the      |
|             |                   |                      | University of       |
|             |                   |                      | Geneva, 2002.       |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | cor               | Compute the absolute | [1] Ciro Castiello, |
|             |                   | value of the         | Giovanna            |
|             |                   | correlation of       | Castellano, and     |
|             |                   | distinct dataset     | Anna Maria Fanelli. |
|             |                   | column pairs.        | Meta-data:          |
|             |                   |                      | Characterization of |
|             |                   |                      | input features for  |
|             |                   |                      | meta-learning. In   |
|             |                   |                      | 2nd International   |
|             |                   |                      | Conference on       |
|             |                   |                      | Modeling Decisions  |
|             |                   |                      | for Artificial      |
|             |                   |                      | Intelligence        |
|             |                   |                      | (MDAI), pages       |
|             |                   |                      | 457–468, 2005.      |
|             |                   |                      | [2] Matthias Reif,  |
|             |                   |                      | Faisal Shafait,     |
|             |                   |                      | Markus Goldstein,   |
|             |                   |                      | Thomas Breuel, and  |
|             |                   |                      | Andreas Dengel.     |
|             |                   |                      | Automatic           |
|             |                   |                      | classifier          |
|             |                   |                      | selection for non-  |
|             |                   |                      | experts. Pattern    |
|             |                   |                      | Analysis and        |
|             |                   |                      | Applications,       |
|             |                   |                      | 17(1):83–96, 2014.  |
|             |                   |                      | [3] Donald Michie,  |
|             |                   |                      | David J.            |
|             |                   |                      | Spiegelhalter,      |
|             |                   |                      | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | cov               | Compute the absolute | [1] Ciro Castiello, |
|             |                   | value of the         | Giovanna            |
|             |                   | covariance of        | Castellano, and     |
|             |                   | distinct dataset     | Anna Maria Fanelli. |
|             |                   | attribute pairs.     | Meta-data:          |
|             |                   |                      | Characterization of |
|             |                   |                      | input features for  |
|             |                   |                      | meta-learning. In   |
|             |                   |                      | 2nd International   |
|             |                   |                      | Conference on       |
|             |                   |                      | Modeling Decisions  |
|             |                   |                      | for Artificial      |
|             |                   |                      | Intelligence        |
|             |                   |                      | (MDAI), pages       |
|             |                   |                      | 457–468, 2005.      |
|             |                   |                      | [2] Donald Michie,  |
|             |                   |                      | David J.            |
|             |                   |                      | Spiegelhalter,      |
|             |                   |                      | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | eigenvalues       | Compute the          | [1] Shawkat Ali and |
|             |                   | eigenvalues of       | Kate A. Smith. On   |
|             |                   | covariance matrix    | learning algorithm  |
|             |                   | from dataset.        | selection for       |
|             |                   |                      | classification.     |
|             |                   |                      | Applied Soft        |
|             |                   |                      | Computing, 6(2):119 |
|             |                   |                      | – 138, 2006.        |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | g_mean            | Compute the          | [1] Shawkat Ali and |
|             |                   | geometric mean of    | Kate A. Smith-      |
|             |                   | each attribute.      | Miles. A meta-      |
|             |                   |                      | learning approach   |
|             |                   |                      | to automatic kernel |
|             |                   |                      | selection for       |
|             |                   |                      | support vector      |
|             |                   |                      | machines.           |
|             |                   |                      | Neurocomputing,     |
|             |                   |                      | 70(1):173 – 186,    |
|             |                   |                      | 2006.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | gravity           | Compute the distance | [1] Shawkat Ali and |
|             |                   | between minority and | Kate A. Smith. On   |
|             |                   | majority classes     | learning algorithm  |
|             |                   | center of mass.      | selection for       |
|             |                   |                      | classification.     |
|             |                   |                      | Applied Soft        |
|             |                   |                      | Computing, 6(2):119 |
|             |                   |                      | – 138, 2006.        |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | h_mean            | Compute the harmonic | [1] Shawkat Ali and |
|             |                   | mean of each         | Kate A. Smith-      |
|             |                   | attribute.           | Miles. A meta-      |
|             |                   |                      | learning approach   |
|             |                   |                      | to automatic kernel |
|             |                   |                      | selection for       |
|             |                   |                      | support vector      |
|             |                   |                      | machines.           |
|             |                   |                      | Neurocomputing,     |
|             |                   |                      | 70(1):173 – 186,    |
|             |                   |                      | 2006.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | iq_range          | Compute the          | [1] Shawkat Ali and |
|             |                   | interquartile range  | Kate A. Smith-      |
|             |                   | (IQR) of each        | Miles. A meta-      |
|             |                   | attribute.           | learning approach   |
|             |                   |                      | to automatic kernel |
|             |                   |                      | selection for       |
|             |                   |                      | support vector      |
|             |                   |                      | machines.           |
|             |                   |                      | Neurocomputing,     |
|             |                   |                      | 70(1):173 – 186,    |
|             |                   |                      | 2006.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | kurtosis          | Compute the kurtosis | [1] Donald Michie,  |
|             |                   | of each attribute.   | David J.            |
|             |                   |                      | Spiegelhalter,      |
|             |                   |                      | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | lh_trace          | Compute the Lawley-  | [1] Lawley D. A     |
|             |                   | Hotelling trace.     | Generalization of   |
|             |                   |                      | Fisher’s z Test.    |
|             |                   |                      | Biometrika.         |
|             |                   |                      | 1938;30(1):180-187. |
|             |                   |                      | [2] Hotelling H. A  |
|             |                   |                      | generalized T test  |
|             |                   |                      | and measure of      |
|             |                   |                      | multivariate        |
|             |                   |                      | dispersion. In:     |
|             |                   |                      | Neyman J, ed.       |
|             |                   |                      | Proceedings of the  |
|             |                   |                      | Second Berkeley     |
|             |                   |                      | Symposium on        |
|             |                   |                      | Mathematical        |
|             |                   |                      | Statistics and      |
|             |                   |                      | Probability.        |
|             |                   |                      | Berkeley:           |
|             |                   |                      | University of       |
|             |                   |                      | California Press;   |
|             |                   |                      | 1951:23-41.         |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | mad               | Compute the Median   | [1] Shawkat Ali and |
|             |                   | Absolute Deviation   | Kate A. Smith. On   |
|             |                   | (MAD) adjusted by a  | learning algorithm  |
|             |                   | factor.              | selection for       |
|             |                   |                      | classification.     |
|             |                   |                      | Applied Soft        |
|             |                   |                      | Computing, 6(2):119 |
|             |                   |                      | – 138, 2006.        |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | max               | Compute the maximum  | [1] Robert Engels   |
|             |                   | value from each      | and Christiane      |
|             |                   | attribute.           | Theusinger. Using a |
|             |                   |                      | data metric for     |
|             |                   |                      | preprocessing       |
|             |                   |                      | advice for data     |
|             |                   |                      | mining              |
|             |                   |                      | applications. In    |
|             |                   |                      | 13th European       |
|             |                   |                      | Conference on on    |
|             |                   |                      | Artificial          |
|             |                   |                      | Intelligence        |
|             |                   |                      | (ECAI), pages 430 – |
|             |                   |                      | 434, 1998.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | mean              | Compute the mean     | [1] Robert Engels   |
|             |                   | value of each        | and Christiane      |
|             |                   | attribute.           | Theusinger. Using a |
|             |                   |                      | data metric for     |
|             |                   |                      | preprocessing       |
|             |                   |                      | advice for data     |
|             |                   |                      | mining              |
|             |                   |                      | applications. In    |
|             |                   |                      | 13th European       |
|             |                   |                      | Conference on on    |
|             |                   |                      | Artificial          |
|             |                   |                      | Intelligence        |
|             |                   |                      | (ECAI), pages 430 – |
|             |                   |                      | 434, 1998.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | median            | Compute the median   | [1] Robert Engels   |
|             |                   | value from each      | and Christiane      |
|             |                   | attribute.           | Theusinger. Using a |
|             |                   |                      | data metric for     |
|             |                   |                      | preprocessing       |
|             |                   |                      | advice for data     |
|             |                   |                      | mining              |
|             |                   |                      | applications. In    |
|             |                   |                      | 13th European       |
|             |                   |                      | Conference on on    |
|             |                   |                      | Artificial          |
|             |                   |                      | Intelligence        |
|             |                   |                      | (ECAI), pages 430 – |
|             |                   |                      | 434, 1998.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | min               | Compute the minimum  | [1] Robert Engels   |
|             |                   | value from each      | and Christiane      |
|             |                   | attribute.           | Theusinger. Using a |
|             |                   |                      | data metric for     |
|             |                   |                      | preprocessing       |
|             |                   |                      | advice for data     |
|             |                   |                      | mining              |
|             |                   |                      | applications. In    |
|             |                   |                      | 13th European       |
|             |                   |                      | Conference on on    |
|             |                   |                      | Artificial          |
|             |                   |                      | Intelligence        |
|             |                   |                      | (ECAI), pages 430 – |
|             |                   |                      | 434, 1998.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | nr_cor_attr       | Compute the number   | [1] Mostafa A.      |
|             |                   | of distinct highly   | Salama, Aboul Ella  |
|             |                   | correlated pair of   | Hassanien, and      |
|             |                   | attributes.          | Kenneth Revett.     |
|             |                   |                      | Employment of       |
|             |                   |                      | neural network and  |
|             |                   |                      | rough set in meta-  |
|             |                   |                      | learning. Memetic   |
|             |                   |                      | Computing, 5(3):165 |
|             |                   |                      | – 177, 2013.        |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | nr_disc           | Compute the number   | [1] Guido Lindner   |
|             |                   | of canonical         | and Rudi Studer.    |
|             |                   | correlation between  | AST: Support for    |
|             |                   | each attribute and   | algorithm selection |
|             |                   | class.               | with a CBR          |
|             |                   |                      | approach. In        |
|             |                   |                      | European Conference |
|             |                   |                      | on Principles of    |
|             |                   |                      | Data Mining and     |
|             |                   |                      | Knowledge Discovery |
|             |                   |                      | (PKDD), pages 418 – |
|             |                   |                      | 423, 1999.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | nr_norm           | Compute the number   | [1] Christian Kopf, |
|             |                   | of attributes        | Charles Taylor, and |
|             |                   | normally distributed | Jorg Keller. Meta-  |
|             |                   | based in a given     | Analysis: From data |
|             |                   | method.              | characterisation    |
|             |                   |                      | for meta-learning   |
|             |                   |                      | to meta-regression. |
|             |                   |                      | In PKDD Workshop on |
|             |                   |                      | Data Mining,        |
|             |                   |                      | Decision Support,   |
|             |                   |                      | Meta-Learning and   |
|             |                   |                      | Inductive Logic     |
|             |                   |                      | Programming, pages  |
|             |                   |                      | 15 – 26, 2000.      |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | nr_outliers       | Compute the number   | [1] Christian Kopf  |
|             |                   | of attributes with   | and Ioannis         |
|             |                   | at least one outlier | Iglezakis.          |
|             |                   | value.               | Combination of task |
|             |                   |                      | description         |
|             |                   |                      | strategies and case |
|             |                   |                      | base properties for |
|             |                   |                      | meta-learning. In   |
|             |                   |                      | 2nd ECML/PKDD       |
|             |                   |                      | International       |
|             |                   |                      | Workshop on         |
|             |                   |                      | Integration and     |
|             |                   |                      | Collaboration       |
|             |                   |                      | Aspects of Data     |
|             |                   |                      | Mining, Decision    |
|             |                   |                      | Support and Meta-   |
|             |                   |                      | Learning(IDDM),     |
|             |                   |                      | pages 65 – 76,      |
|             |                   |                      | 2002.               |
|             |                   |                      | [2] Peter J.        |
|             |                   |                      | Rousseeuw and Mia   |
|             |                   |                      | Hubert. Robust      |
|             |                   |                      | statistics for      |
|             |                   |                      | outlier detection.  |
|             |                   |                      | Wiley               |
|             |                   |                      | Interdisciplinary   |
|             |                   |                      | Reviews: Data       |
|             |                   |                      | Mining and          |
|             |                   |                      | Knowledge           |
|             |                   |                      | Discovery, 1(1):73  |
|             |                   |                      | – 79, 2011.         |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | p_trace           | Compute the Pillai's | [1] Pillai K.C.S    |
|             |                   | trace.               | (1955). Some New    |
|             |                   |                      | test criteria in    |
|             |                   |                      | multivariate        |
|             |                   |                      | analysis. Ann Math  |
|             |                   |                      | Stat: 26(1):117–21. |
|             |                   |                      | Seber, G.A.F.       |
|             |                   |                      | (1984).             |
|             |                   |                      | Multivariate        |
|             |                   |                      | Observations. New   |
|             |                   |                      | York: John Wiley    |
|             |                   |                      | and Sons.           |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | range             | Compute the range    | [1] Shawkat Ali and |
|             |                   | (max - min) of each  | Kate A. Smith-      |
|             |                   | attribute.           | Miles. A meta-      |
|             |                   |                      | learning approach   |
|             |                   |                      | to automatic kernel |
|             |                   |                      | selection for       |
|             |                   |                      | support vector      |
|             |                   |                      | machines.           |
|             |                   |                      | Neurocomputing,     |
|             |                   |                      | 70(1):173 – 186,    |
|             |                   |                      | 2006.               |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | roy_root          | Compute the Roy's    | [1] Roy SN. On a    |
|             |                   | largest root.        | Heuristic Method of |
|             |                   |                      | Test Construction   |
|             |                   |                      | and its use in      |
|             |                   |                      | Multivariate        |
|             |                   |                      | Analysis. Ann Math  |
|             |                   |                      | Stat.               |
|             |                   |                      | 1953;24(2):220-238. |
|             |                   |                      | [2] A note on Roy's |
|             |                   |                      | largest root.       |
|             |                   |                      | Kuhfeld, W.F.       |
|             |                   |                      | Psychometrika       |
|             |                   |                      | (1986) 51: 479. htt |
|             |                   |                      | ps://doi.org/10.100 |
|             |                   |                      | 7/BF02294069        |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | sd                | Compute the standard | [1] Robert Engels   |
|             |                   | deviation of each    | and Christiane      |
|             |                   | attribute.           | Theusinger. Using a |
|             |                   |                      | data metric for     |
|             |                   |                      | preprocessing       |
|             |                   |                      | advice for data     |
|             |                   |                      | mining              |
|             |                   |                      | applications. In    |
|             |                   |                      | 13th European       |
|             |                   |                      | Conference on on    |
|             |                   |                      | Artificial          |
|             |                   |                      | Intelligence        |
|             |                   |                      | (ECAI), pages 430 – |
|             |                   |                      | 434, 1998.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | sd_ratio          | Compute a            | [1] Donald Michie,  |
|             |                   | statistical test for | David J.            |
|             |                   | homogeneity of       | Spiegelhalter,      |
|             |                   | covariances.         | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | skewness          | Compute the skewness | [1] Donald Michie,  |
|             |                   | for each attribute.  | David J.            |
|             |                   |                      | Spiegelhalter,      |
|             |                   |                      | Charles C. Taylor,  |
|             |                   |                      | and John Campbell.  |
|             |                   |                      | Machine Learning,   |
|             |                   |                      | Neural and          |
|             |                   |                      | Statistical         |
|             |                   |                      | Classification,     |
|             |                   |                      | volume 37. Ellis    |
|             |                   |                      | Horwood Upper       |
|             |                   |                      | Saddle River, 1994. |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | sparsity          | Compute (possibly    | [1] Mostafa A.      |
|             |                   | normalized) sparsity | Salama, Aboul Ella  |
|             |                   | metric for each      | Hassanien, and      |
|             |                   | attribute.           | Kenneth Revett.     |
|             |                   |                      | Employment of       |
|             |                   |                      | neural network and  |
|             |                   |                      | rough set in meta-  |
|             |                   |                      | learning. Memetic   |
|             |                   |                      | Computing, 5(3):165 |
|             |                   |                      | – 177, 2013.        |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | t_mean            | Compute the trimmed  | [1] Robert Engels   |
|             |                   | mean of each         | and Christiane      |
|             |                   | attribute.           | Theusinger. Using a |
|             |                   |                      | data metric for     |
|             |                   |                      | preprocessing       |
|             |                   |                      | advice for data     |
|             |                   |                      | mining              |
|             |                   |                      | applications. In    |
|             |                   |                      | 13th European       |
|             |                   |                      | Conference on on    |
|             |                   |                      | Artificial          |
|             |                   |                      | Intelligence        |
|             |                   |                      | (ECAI), pages 430 – |
|             |                   |                      | 434, 1998.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | var               | Compute the variance | [1] Ciro Castiello, |
|             |                   | of each attribute.   | Giovanna            |
|             |                   |                      | Castellano, and     |
|             |                   |                      | Anna Maria Fanelli. |
|             |                   |                      | Meta-data:          |
|             |                   |                      | Characterization of |
|             |                   |                      | input features for  |
|             |                   |                      | meta-learning. In   |
|             |                   |                      | 2nd International   |
|             |                   |                      | Conference on       |
|             |                   |                      | Modeling Decisions  |
|             |                   |                      | for Artificial      |
|             |                   |                      | Intelligence        |
|             |                   |                      | (MDAI), pages       |
|             |                   |                      | 457–468, 2005.      |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+
| statistical | w_lambda          | Compute the Wilks'   | [1] Guido Lindner   |
|             |                   | Lambda value.        | and Rudi Studer.    |
|             |                   |                      | AST: Support for    |
|             |                   |                      | algorithm selection |
|             |                   |                      | with a CBR          |
|             |                   |                      | approach. In        |
|             |                   |                      | European Conference |
|             |                   |                      | on Principles of    |
|             |                   |                      | Data Mining and     |
|             |                   |                      | Knowledge Discovery |
|             |                   |                      | (PKDD), pages 418 – |
|             |                   |                      | 423, 1999.          |
|             |                   |                      |                     |
+-------------+-------------------+----------------------+---------------------+

You also can get the table instead of printing it.

MFE.metafeature_description(print_table=False)
([['Group', 'Meta-feature name', 'Description'], ['clustering', 'ch', 'Compute the Calinski and Harabasz index.'], ['clustering', 'int', 'Compute the INT index.'], ['clustering', 'nre', 'Compute the normalized relative entropy.'], ['clustering', 'pb', 'Compute the pearson correlation between class matching and instance distances.'], ['clustering', 'sc', 'Compute the number of clusters with size smaller than a given size.'], ['clustering', 'sil', 'Compute the mean silhouette value.'], ['clustering', 'vdb', 'Compute the Davies and Bouldin Index.'], ['clustering', 'vdu', 'Compute the Dunn Index.'], ['complexity', 'c1', 'Compute the entropy of class proportions.'], ['complexity', 'c2', 'Compute the imbalance ratio.'], ['complexity', 'cls_coef', 'Clustering coefficient.'], ['complexity', 'density', 'Average density of the network.'], ['complexity', 'f1', "Maximum Fisher's discriminant ratio."], ['complexity', 'f1v', "Directional-vector maximum Fisher's discriminant ratio."], ['complexity', 'f2', 'Volume of the overlapping region.'], ['complexity', 'f3', 'Compute feature maximum individual efficiency.'], ['complexity', 'f4', 'Compute the collective feature efficiency.'], ['complexity', 'hubs', 'Hub score.'], ['complexity', 'l1', 'Sum of error distance by linear programming.'], ['complexity', 'l2', 'Compute the OVO subsets error rate of linear classifier.'], ['complexity', 'l3', 'Non-Linearity of a linear classifier.'], ['complexity', 'lsc', 'Local set average cardinality.'], ['complexity', 'n1', 'Compute the fraction of borderline points.'], ['complexity', 'n2', 'Ratio of intra and extra class nearest neighbor distance.'], ['complexity', 'n3', 'Error rate of the nearest neighbor classifier.'], ['complexity', 'n4', 'Compute the non-linearity of the k-NN Classifier.'], ['complexity', 't1', 'Fraction of hyperspheres covering data.'], ['complexity', 't2', 'Compute the average number of features per dimension.'], ['complexity', 't3', 'Compute the average number of PCA dimensions per points.'], ['complexity', 't4', 'Compute the ratio of the PCA dimension to the original dimension.'], ['concept', 'cohesiveness', 'Compute the improved version of the weighted distance, that captures how dense or sparse is the example distribution.'], ['concept', 'conceptvar', 'Compute the concept variation that estimates the variability of class labels among examples.'], ['concept', 'impconceptvar', 'Compute the improved concept variation that estimates the variability of class labels among examples.'], ['concept', 'wg_dist', 'Compute the weighted distance, that captures how dense or sparse is the example distribution.'], ['info-theory', 'attr_conc', 'Compute concentration coef. of each pair of distinct attributes.'], ['info-theory', 'attr_ent', "Compute Shannon's entropy for each predictive attribute."], ['info-theory', 'class_conc', 'Compute concentration coefficient between each attribute and class.'], ['info-theory', 'class_ent', "Compute target attribute Shannon's entropy."], ['info-theory', 'eq_num_attr', 'Compute the number of attributes equivalent for a predictive task.'], ['info-theory', 'joint_ent', 'Compute the joint entropy between each attribute and class.'], ['info-theory', 'mut_inf', 'Compute the mutual information between each attribute and target.'], ['info-theory', 'ns_ratio', 'Compute the noisiness of attributes.'], ['landmarking', 'best_node', 'Performance of a the best single decision tree node.'], ['landmarking', 'elite_nn', 'Performance of Elite Nearest Neighbor.'], ['landmarking', 'linear_discr', 'Performance of the Linear Discriminant classifier.'], ['landmarking', 'naive_bayes', 'Performance of the Naive Bayes classifier.'], ['landmarking', 'one_nn', 'Performance of the 1-Nearest Neighbor classifier.'], ['landmarking', 'random_node', 'Performance of the single decision tree node model induced by a random attribute.'], ['landmarking', 'worst_node', 'Performance of the single decision tree node model induced by the worst informative attribute.'], ['general', 'attr_to_inst', 'Compute the ratio between the number of attributes.'], ['general', 'cat_to_num', 'Compute the ratio between the number of categoric and numeric features.'], ['general', 'freq_class', 'Compute the relative frequency of each distinct class.'], ['general', 'inst_to_attr', 'Compute the ratio between the number of instances and attributes.'], ['general', 'nr_attr', 'Compute the total number of attributes.'], ['general', 'nr_bin', 'Compute the number of binary attributes.'], ['general', 'nr_cat', 'Compute the number of categorical attributes.'], ['general', 'nr_class', 'Compute the number of distinct classes.'], ['general', 'nr_inst', 'Compute the number of instances (rows) in the dataset.'], ['general', 'nr_num', 'Compute the number of numeric features.'], ['general', 'num_to_cat', 'Compute the number of numerical and categorical features.'], ['statistical', 'can_cor', 'Compute canonical correlations of data.'], ['statistical', 'cor', 'Compute the absolute value of the correlation of distinct dataset column pairs.'], ['statistical', 'cov', 'Compute the absolute value of the covariance of distinct dataset attribute pairs.'], ['statistical', 'eigenvalues', 'Compute the eigenvalues of covariance matrix from dataset.'], ['statistical', 'g_mean', 'Compute the geometric mean of each attribute.'], ['statistical', 'gravity', 'Compute the distance between minority and majority classes center of mass.'], ['statistical', 'h_mean', 'Compute the harmonic mean of each attribute.'], ['statistical', 'iq_range', 'Compute the interquartile range (IQR) of each attribute.'], ['statistical', 'kurtosis', 'Compute the kurtosis of each attribute.'], ['statistical', 'lh_trace', 'Compute the Lawley-Hotelling trace.'], ['statistical', 'mad', 'Compute the Median Absolute Deviation (MAD) adjusted by a factor.'], ['statistical', 'max', 'Compute the maximum value from each attribute.'], ['statistical', 'mean', 'Compute the mean value of each attribute.'], ['statistical', 'median', 'Compute the median value from each attribute.'], ['statistical', 'min', 'Compute the minimum value from each attribute.'], ['statistical', 'nr_cor_attr', 'Compute the number of distinct highly correlated pair of attributes.'], ['statistical', 'nr_disc', 'Compute the number of canonical correlation between each attribute and class.'], ['statistical', 'nr_norm', 'Compute the number of attributes normally distributed based in a given method.'], ['statistical', 'nr_outliers', 'Compute the number of attributes with at least one outlier value.'], ['statistical', 'p_trace', "Compute the Pillai's trace."], ['statistical', 'range', 'Compute the range (max - min) of each attribute.'], ['statistical', 'roy_root', "Compute the Roy's largest root."], ['statistical', 'sd', 'Compute the standard deviation of each attribute.'], ['statistical', 'sd_ratio', 'Compute a statistical test for homogeneity of covariances.'], ['statistical', 'skewness', 'Compute the skewness for each attribute.'], ['statistical', 'sparsity', 'Compute (possibly normalized) sparsity metric for each attribute.'], ['statistical', 't_mean', 'Compute the trimmed mean of each attribute.'], ['statistical', 'var', 'Compute the variance of each attribute.'], ['statistical', 'w_lambda', "Compute the Wilks' Lambda value."], ['model-based', 'leaves', 'Compute the number of leaf nodes in the DT model.'], ['model-based', 'leaves_branch', 'Compute the size of branches in the DT model.'], ['model-based', 'leaves_corrob', 'Compute the leaves corroboration of the DT model.'], ['model-based', 'leaves_homo', 'Compute the DT model Homogeneity for every leaf node.'], ['model-based', 'leaves_per_class', 'Compute the proportion of leaves per class in DT model.'], ['model-based', 'nodes', 'Compute the number of non-leaf nodes in DT model.'], ['model-based', 'nodes_per_attr', 'Compute the ratio of nodes per number of attributes in DT model.'], ['model-based', 'nodes_per_inst', 'Compute the ratio of non-leaf nodes per number of instances in DT model.'], ['model-based', 'nodes_per_level', 'Compute the ratio of number of nodes per tree level in DT model.'], ['model-based', 'nodes_repeated', 'Compute the number of repeated nodes in DT model.'], ['model-based', 'tree_depth', 'Compute the depth of every node in the DT model.'], ['model-based', 'tree_imbalance', 'Compute the tree imbalance for each leaf node.'], ['model-based', 'tree_shape', 'Compute the tree shape for every leaf node.'], ['model-based', 'var_importance', 'Compute the features importance of the DT model for each attribute.'], ['itemset', 'one_itemset', 'Compute the one itemset meta-feature.'], ['itemset', 'two_itemset', 'Compute the two itemset meta-feature.']], "+-------------+-------------------+--------------------------------------------+\n|    Group    | Meta-feature name |                Description                 |\n+=============+===================+============================================+\n| clustering  | ch                | Compute the Calinski and Harabasz index.   |\n+-------------+-------------------+--------------------------------------------+\n| clustering  | int               | Compute the INT index.                     |\n+-------------+-------------------+--------------------------------------------+\n| clustering  | nre               | Compute the normalized relative entropy.   |\n+-------------+-------------------+--------------------------------------------+\n| clustering  | pb                | Compute the pearson correlation between    |\n|             |                   | class matching and instance distances.     |\n+-------------+-------------------+--------------------------------------------+\n| clustering  | sc                | Compute the number of clusters with size   |\n|             |                   | smaller than a given size.                 |\n+-------------+-------------------+--------------------------------------------+\n| clustering  | sil               | Compute the mean silhouette value.         |\n+-------------+-------------------+--------------------------------------------+\n| clustering  | vdb               | Compute the Davies and Bouldin Index.      |\n+-------------+-------------------+--------------------------------------------+\n| clustering  | vdu               | Compute the Dunn Index.                    |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | c1                | Compute the entropy of class proportions.  |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | c2                | Compute the imbalance ratio.               |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | cls_coef          | Clustering coefficient.                    |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | density           | Average density of the network.            |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | f1                | Maximum Fisher's discriminant ratio.       |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | f1v               | Directional-vector maximum Fisher's        |\n|             |                   | discriminant ratio.                        |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | f2                | Volume of the overlapping region.          |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | f3                | Compute feature maximum individual         |\n|             |                   | efficiency.                                |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | f4                | Compute the collective feature efficiency. |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | hubs              | Hub score.                                 |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | l1                | Sum of error distance by linear            |\n|             |                   | programming.                               |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | l2                | Compute the OVO subsets error rate of      |\n|             |                   | linear classifier.                         |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | l3                | Non-Linearity of a linear classifier.      |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | lsc               | Local set average cardinality.             |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | n1                | Compute the fraction of borderline points. |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | n2                | Ratio of intra and extra class nearest     |\n|             |                   | neighbor distance.                         |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | n3                | Error rate of the nearest neighbor         |\n|             |                   | classifier.                                |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | n4                | Compute the non-linearity of the k-NN      |\n|             |                   | Classifier.                                |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | t1                | Fraction of hyperspheres covering data.    |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | t2                | Compute the average number of features per |\n|             |                   | dimension.                                 |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | t3                | Compute the average number of PCA          |\n|             |                   | dimensions per points.                     |\n+-------------+-------------------+--------------------------------------------+\n| complexity  | t4                | Compute the ratio of the PCA dimension to  |\n|             |                   | the original dimension.                    |\n+-------------+-------------------+--------------------------------------------+\n| concept     | cohesiveness      | Compute the improved version of the        |\n|             |                   | weighted distance, that captures how dense |\n|             |                   | or sparse is the example distribution.     |\n+-------------+-------------------+--------------------------------------------+\n| concept     | conceptvar        | Compute the concept variation that         |\n|             |                   | estimates the variability of class labels  |\n|             |                   | among examples.                            |\n+-------------+-------------------+--------------------------------------------+\n| concept     | impconceptvar     | Compute the improved concept variation     |\n|             |                   | that estimates the variability of class    |\n|             |                   | labels among examples.                     |\n+-------------+-------------------+--------------------------------------------+\n| concept     | wg_dist           | Compute the weighted distance, that        |\n|             |                   | captures how dense or sparse is the        |\n|             |                   | example distribution.                      |\n+-------------+-------------------+--------------------------------------------+\n| info-theory | attr_conc         | Compute concentration coef. of each pair   |\n|             |                   | of distinct attributes.                    |\n+-------------+-------------------+--------------------------------------------+\n| info-theory | attr_ent          | Compute Shannon's entropy for each         |\n|             |                   | predictive attribute.                      |\n+-------------+-------------------+--------------------------------------------+\n| info-theory | class_conc        | Compute concentration coefficient between  |\n|             |                   | each attribute and class.                  |\n+-------------+-------------------+--------------------------------------------+\n| info-theory | class_ent         | Compute target attribute Shannon's         |\n|             |                   | entropy.                                   |\n+-------------+-------------------+--------------------------------------------+\n| info-theory | eq_num_attr       | Compute the number of attributes           |\n|             |                   | equivalent for a predictive task.          |\n+-------------+-------------------+--------------------------------------------+\n| info-theory | joint_ent         | Compute the joint entropy between each     |\n|             |                   | attribute and class.                       |\n+-------------+-------------------+--------------------------------------------+\n| info-theory | mut_inf           | Compute the mutual information between     |\n|             |                   | each attribute and target.                 |\n+-------------+-------------------+--------------------------------------------+\n| info-theory | ns_ratio          | Compute the noisiness of attributes.       |\n+-------------+-------------------+--------------------------------------------+\n| landmarking | best_node         | Performance of a the best single decision  |\n|             |                   | tree node.                                 |\n+-------------+-------------------+--------------------------------------------+\n| landmarking | elite_nn          | Performance of Elite Nearest Neighbor.     |\n+-------------+-------------------+--------------------------------------------+\n| landmarking | linear_discr      | Performance of the Linear Discriminant     |\n|             |                   | classifier.                                |\n+-------------+-------------------+--------------------------------------------+\n| landmarking | naive_bayes       | Performance of the Naive Bayes classifier. |\n+-------------+-------------------+--------------------------------------------+\n| landmarking | one_nn            | Performance of the 1-Nearest Neighbor      |\n|             |                   | classifier.                                |\n+-------------+-------------------+--------------------------------------------+\n| landmarking | random_node       | Performance of the single decision tree    |\n|             |                   | node model induced by a random attribute.  |\n+-------------+-------------------+--------------------------------------------+\n| landmarking | worst_node        | Performance of the single decision tree    |\n|             |                   | node model induced by the worst            |\n|             |                   | informative attribute.                     |\n+-------------+-------------------+--------------------------------------------+\n| general     | attr_to_inst      | Compute the ratio between the number of    |\n|             |                   | attributes.                                |\n+-------------+-------------------+--------------------------------------------+\n| general     | cat_to_num        | Compute the ratio between the number of    |\n|             |                   | categoric and numeric features.            |\n+-------------+-------------------+--------------------------------------------+\n| general     | freq_class        | Compute the relative frequency of each     |\n|             |                   | distinct class.                            |\n+-------------+-------------------+--------------------------------------------+\n| general     | inst_to_attr      | Compute the ratio between the number of    |\n|             |                   | instances and attributes.                  |\n+-------------+-------------------+--------------------------------------------+\n| general     | nr_attr           | Compute the total number of attributes.    |\n+-------------+-------------------+--------------------------------------------+\n| general     | nr_bin            | Compute the number of binary attributes.   |\n+-------------+-------------------+--------------------------------------------+\n| general     | nr_cat            | Compute the number of categorical          |\n|             |                   | attributes.                                |\n+-------------+-------------------+--------------------------------------------+\n| general     | nr_class          | Compute the number of distinct classes.    |\n+-------------+-------------------+--------------------------------------------+\n| general     | nr_inst           | Compute the number of instances (rows) in  |\n|             |                   | the dataset.                               |\n+-------------+-------------------+--------------------------------------------+\n| general     | nr_num            | Compute the number of numeric features.    |\n+-------------+-------------------+--------------------------------------------+\n| general     | num_to_cat        | Compute the number of numerical and        |\n|             |                   | categorical features.                      |\n+-------------+-------------------+--------------------------------------------+\n| statistical | can_cor           | Compute canonical correlations of data.    |\n+-------------+-------------------+--------------------------------------------+\n| statistical | cor               | Compute the absolute value of the          |\n|             |                   | correlation of distinct dataset column     |\n|             |                   | pairs.                                     |\n+-------------+-------------------+--------------------------------------------+\n| statistical | cov               | Compute the absolute value of the          |\n|             |                   | covariance of distinct dataset attribute   |\n|             |                   | pairs.                                     |\n+-------------+-------------------+--------------------------------------------+\n| statistical | eigenvalues       | Compute the eigenvalues of covariance      |\n|             |                   | matrix from dataset.                       |\n+-------------+-------------------+--------------------------------------------+\n| statistical | g_mean            | Compute the geometric mean of each         |\n|             |                   | attribute.                                 |\n+-------------+-------------------+--------------------------------------------+\n| statistical | gravity           | Compute the distance between minority and  |\n|             |                   | majority classes center of mass.           |\n+-------------+-------------------+--------------------------------------------+\n| statistical | h_mean            | Compute the harmonic mean of each          |\n|             |                   | attribute.                                 |\n+-------------+-------------------+--------------------------------------------+\n| statistical | iq_range          | Compute the interquartile range (IQR) of   |\n|             |                   | each attribute.                            |\n+-------------+-------------------+--------------------------------------------+\n| statistical | kurtosis          | Compute the kurtosis of each attribute.    |\n+-------------+-------------------+--------------------------------------------+\n| statistical | lh_trace          | Compute the Lawley-Hotelling trace.        |\n+-------------+-------------------+--------------------------------------------+\n| statistical | mad               | Compute the Median Absolute Deviation      |\n|             |                   | (MAD) adjusted by a factor.                |\n+-------------+-------------------+--------------------------------------------+\n| statistical | max               | Compute the maximum value from each        |\n|             |                   | attribute.                                 |\n+-------------+-------------------+--------------------------------------------+\n| statistical | mean              | Compute the mean value of each attribute.  |\n+-------------+-------------------+--------------------------------------------+\n| statistical | median            | Compute the median value from each         |\n|             |                   | attribute.                                 |\n+-------------+-------------------+--------------------------------------------+\n| statistical | min               | Compute the minimum value from each        |\n|             |                   | attribute.                                 |\n+-------------+-------------------+--------------------------------------------+\n| statistical | nr_cor_attr       | Compute the number of distinct highly      |\n|             |                   | correlated pair of attributes.             |\n+-------------+-------------------+--------------------------------------------+\n| statistical | nr_disc           | Compute the number of canonical            |\n|             |                   | correlation between each attribute and     |\n|             |                   | class.                                     |\n+-------------+-------------------+--------------------------------------------+\n| statistical | nr_norm           | Compute the number of attributes normally  |\n|             |                   | distributed based in a given method.       |\n+-------------+-------------------+--------------------------------------------+\n| statistical | nr_outliers       | Compute the number of attributes with at   |\n|             |                   | least one outlier value.                   |\n+-------------+-------------------+--------------------------------------------+\n| statistical | p_trace           | Compute the Pillai's trace.                |\n+-------------+-------------------+--------------------------------------------+\n| statistical | range             | Compute the range (max - min) of each      |\n|             |                   | attribute.                                 |\n+-------------+-------------------+--------------------------------------------+\n| statistical | roy_root          | Compute the Roy's largest root.            |\n+-------------+-------------------+--------------------------------------------+\n| statistical | sd                | Compute the standard deviation of each     |\n|             |                   | attribute.                                 |\n+-------------+-------------------+--------------------------------------------+\n| statistical | sd_ratio          | Compute a statistical test for homogeneity |\n|             |                   | of covariances.                            |\n+-------------+-------------------+--------------------------------------------+\n| statistical | skewness          | Compute the skewness for each attribute.   |\n+-------------+-------------------+--------------------------------------------+\n| statistical | sparsity          | Compute (possibly normalized) sparsity     |\n|             |                   | metric for each attribute.                 |\n+-------------+-------------------+--------------------------------------------+\n| statistical | t_mean            | Compute the trimmed mean of each           |\n|             |                   | attribute.                                 |\n+-------------+-------------------+--------------------------------------------+\n| statistical | var               | Compute the variance of each attribute.    |\n+-------------+-------------------+--------------------------------------------+\n| statistical | w_lambda          | Compute the Wilks' Lambda value.           |\n+-------------+-------------------+--------------------------------------------+\n| model-based | leaves            | Compute the number of leaf nodes in the DT |\n|             |                   | model.                                     |\n+-------------+-------------------+--------------------------------------------+\n| model-based | leaves_branch     | Compute the size of branches in the DT     |\n|             |                   | model.                                     |\n+-------------+-------------------+--------------------------------------------+\n| model-based | leaves_corrob     | Compute the leaves corroboration of the DT |\n|             |                   | model.                                     |\n+-------------+-------------------+--------------------------------------------+\n| model-based | leaves_homo       | Compute the DT model Homogeneity for every |\n|             |                   | leaf node.                                 |\n+-------------+-------------------+--------------------------------------------+\n| model-based | leaves_per_class  | Compute the proportion of leaves per class |\n|             |                   | in DT model.                               |\n+-------------+-------------------+--------------------------------------------+\n| model-based | nodes             | Compute the number of non-leaf nodes in DT |\n|             |                   | model.                                     |\n+-------------+-------------------+--------------------------------------------+\n| model-based | nodes_per_attr    | Compute the ratio of nodes per number of   |\n|             |                   | attributes in DT model.                    |\n+-------------+-------------------+--------------------------------------------+\n| model-based | nodes_per_inst    | Compute the ratio of non-leaf nodes per    |\n|             |                   | number of instances in DT model.           |\n+-------------+-------------------+--------------------------------------------+\n| model-based | nodes_per_level   | Compute the ratio of number of nodes per   |\n|             |                   | tree level in DT model.                    |\n+-------------+-------------------+--------------------------------------------+\n| model-based | nodes_repeated    | Compute the number of repeated nodes in DT |\n|             |                   | model.                                     |\n+-------------+-------------------+--------------------------------------------+\n| model-based | tree_depth        | Compute the depth of every node in the DT  |\n|             |                   | model.                                     |\n+-------------+-------------------+--------------------------------------------+\n| model-based | tree_imbalance    | Compute the tree imbalance for each leaf   |\n|             |                   | node.                                      |\n+-------------+-------------------+--------------------------------------------+\n| model-based | tree_shape        | Compute the tree shape for every leaf      |\n|             |                   | node.                                      |\n+-------------+-------------------+--------------------------------------------+\n| model-based | var_importance    | Compute the features importance of the DT  |\n|             |                   | model for each attribute.                  |\n+-------------+-------------------+--------------------------------------------+\n| itemset     | one_itemset       | Compute the one itemset meta-feature.      |\n+-------------+-------------------+--------------------------------------------+\n| itemset     | two_itemset       | Compute the two itemset meta-feature.      |\n+-------------+-------------------+--------------------------------------------+")

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