multilabel-eval-metrics 0.0.1

Quickly evaluate multi-label classifiers in various metrics

Evaluation metrics for multi-label classification models

This toolkit is used to focus on different evaluation metrics that can be used for evaluating the performance of a multilabel classifier.

Intro

The evaluation metrics for multi-label classification can be broadly classified into two categories:

• Example-Based Evaluation Metrics
• Label Based Evaluation Metrics.

Metrics

Exact Match Ratio (EMR) 1/0 Loss Hamming Loss Example-Based Accuracy Example-Based Precision Label Based Metrics Macro Averaged Accuracy Macro Averaged Precision Macro Averaged Recall Micro Averaged Accuracy Micro Averaged Precision Micro Averaged Recall α- Evaluation Score

Reference

Examples

from multilabel_eval_metrics import *
import numpy as np
if __name__=="__main__":
    y_true = np.array([[0, 1], [1, 1], [1, 1], [0, 1], [1, 0]])
    y_pred = np.array([[1, 1], [1, 0], [1, 1], [0, 1], [1, 0]])
    print(y_true)
    print(y_pred)
    result=MultiLabelMetrics(y_true,y_pred).get_metric_summary(show=True)

License

The multilabel-eval-metrics toolkit is provided by Donghua Chen with MIT License.