FateWithShellmiaoComment/Fate/doc/federatedml_component/evaluation.md

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)
20. PSI: Compute Population Stability Index.
21. F1-Score: Compute F1-Score for binary tasks.