multilabel-eval-metrics 0.0.2

Quickly evaluate multi-label classifiers in various metrics

MultiLabel Classifier Evaluation Metrics

This toolkit focuses 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

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.

Reference

Evaluation Metrics for Multi-Label Classification