Evaluation

This module provides evaluation methods for classification, regression and clustering. Available metrics include:

  1. AUC: Compute AUC for binary classification.

  2. KS: Compute Kolmogorov-Smirnov for binary classification.

  3. LIFT: Compute lift of binary classification.

  4. PRECISION: Compute the precision for binary and multi-classification

  5. RECALL: Compute the recall for binary and multi-classification

  6. ACCURACY: Compute the accuracy for binary and multi-classification

  7. EXPLAINED_VARIANCE: Compute explain variance for regression tasks

  8. MEAN_ABSOLUTE_ERROR: Compute mean absolute error for regression tasks

  9. MEAN_SQUARED_ERROR: Compute mean square error for regression tasks

  10. MEAN_SQUARED_LOG_ERROR: Compute mean squared logarithmic error for regression tasks

  11. MEDIAN_ABSOLUTE_ERROR: Compute median absolute error for regression tasks

  12. R2_SCORE: Compute R^2 (coefficient of determination) score for regression tasks

  13. ROOT_MEAN_SQUARED_ERROR: Compute the root of mean square error for regression tasks

  14. JACCARD_SIMILARITY_SCORE：Compute Jaccard similarity score for clustering tasks (labels are needed)

  15. ADJUSTED_RAND_SCORE：Compute adjusted rand score for clustering tasks (labels are needed)

  16. FOWLKES_MALLOWS_SCORE：Compute Fowlkes Mallows score for clustering tasks (labels are needed)

  17. DAVIES_BOULDIN_INDEX：Compute Davies Bouldin index for clustering tasks

  18. DISTANCE_MEASURE:Compute cluster information in clustering algorithms

  19. CONTINGENCY_MATRIX:Compute contingency matrix for clustering tasks (labels are needed)

Param

class EvaluateParam(eval_type='binary', pos_label=1, need_run=True, metrics=None, run_clustering_arbiter_metric=False, unfold_multi_result=False)

Define the evaluation method of binary/multiple classification and regression

Parameters

  • eval_type (string, support 'binary' for HomoLR, HeteroLR and Secureboosting. support 'regression' for Secureboosting. 'multi' is not support these version) –

  • unfold_multi_result (bool, unfold multi result and get several one-vs-rest binary classification results) –

  • pos_label (specify positive label type, can be int, float and str, this depend on the data's label, this parameter effective only for 'binary') –

  • need_run (bool, default True) – Indicate if this module needed to be run