This module provides some evaluation method for classification and regression. It contains:

  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 multiple classification

  5. RECALL: Compute the recall for binary and multiple classification

  6. ACCURACY: Compute the accuracy for binary and multiple classification

  7. EXPLAINED_VARIANCE: Compute explain variance

  8. MEAN_ABSOLUTE_ERROR: Compute mean absolute error

  9. MEAN_SQUARED_ERROR: Compute mean square error

  10. MEAN_SQUARED_LOG_ERROR: Compute mean squared logarithmic error

  11. MEDIAN_ABSOLUTE_ERROR: Compute median absolute error

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

  13. ROOT_MEAN_SQUARED_ERROR: Compute the root of mean square error

All of the evaluation metrics above can be used for classification, while regression only support EXPLAINED_VARIANCE, MEAN_ABSOLUTE_ERROR, MEAN_SQUARED_ERROR, MEAN_SQUARED_LOG_ERROR, MEDIAN_ABSOLUTE_ERROR, R2_SCORE, ROOT_MEAN_SQUARED_ERROR


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

Define the evaluation method of binary/multiple classification and regression

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

  • 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