Python Scikit Learn Metrics - NDCG Score














































Python Scikit Learn Metrics - NDCG Score



Introduction:


*It computes Normalized Discounted Cumulative Gain.

*This function sums up the true scores ranked in the order induced by the predicted scores, after applying a logarithmic discount. 

*The following step is to divide by the best possible score to obtain a score between 0 and 1.

*It yields a high value if true labels are ranked high by y_score.


Parameters:


*y_true : array like structure

*y_score : array like structure



Returns:

*Normalized discounted cumulative gain - a float value.



Implementation:


from sklearn.metrics import ndcg_score
true_relevance = np.asarray([[8, 1, 0, 2, 5]])
scores = np.asarray([[.1, .2, .3, 4, 70]])
print(ndcg_score(true_relevance, scores))


Output:


0.7776802164125916



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