### 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([[81025]])
scores = np.asarray([[.1.2.3470]])
print(ndcg_score(true_relevance, scores))

### Output:

0.7776802164125916

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