sklearn.metrics
.ndcg_score¶
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sklearn.metrics.
ndcg_score
(y_true, y_score, k=5)[source]¶ Normalized discounted cumulative gain (NDCG) at rank K.
Normalized Discounted Cumulative Gain (NDCG) measures the performance of a recommendation system based on the graded relevance of the recommended entities. It varies from 0.0 to 1.0, with 1.0 representing the ideal ranking of the entities.
Parameters: y_true : array, shape = [n_samples]
Ground truth (true labels represended as integers).
y_score : array, shape = [n_samples, n_classes]
Predicted probabilities.
k : int
Rank.
Returns: score : float
References
[R232] Kaggle entry for the Normalized Discounted Cumulative Gain Examples
>>> y_true = [1, 0, 2] >>> y_score = [[0.15, 0.55, 0.2], [0.7, 0.2, 0.1], [0.06, 0.04, 0.9]] >>> ndcg_score(y_true, y_score, k=2) 1.0 >>> y_score = [[0.9, 0.5, 0.8], [0.7, 0.2, 0.1], [0.06, 0.04, 0.9]] >>> ndcg_score(y_true, y_score, k=2) 0.66666666666666663