MultiLabel Ranking Metrics – Ranking Loss | ML
Code: Python code to implement Ranking Loss using the scikit-learn library.
# import sklearn and numpy libraries import numpy as np from sklearn.metrics import label_ranking_loss # take sample dataset y_true = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) y_pred_score = np.array([[0.75, 0.5, 1], [1, 0.2, 0.1], [0.1, 1, 0.9]]) # calculate and print label ranking loss print(label_ranking_loss(y_true, y_pred_score )) # this will give minimum ranking loss y_pred_score = np.array([[0.75, 0.5, 0.1], [0.1, 0.6, 0.1], [0.3, 0.3, 0.4]]) print(label_ranking_loss(y_true, y_pred_score )) |
Output:
0.5 0
0
References:
- Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In Data mining and knowledge discovery handbook (pp. 667-685). Springer US.
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