# 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.

<!-- mkdocs
## Param

::: federatedml.param.evaluation_param
    rendering:
      heading_level: 3
      show_source: true
      show_root_heading: true
      show_root_toc_entry: false
      show_root_full_path: false
-->