sklearn.metrics.accuracy_score()

sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None) [source]

Accuracy classification score.

In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true.

Read more in the User Guide.

Parameters:

y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) labels. y_pred : 1d array-like, or label indicator array / sparse matrix Predicted labels, as returned by a classifier. normalize : bool, optional (default=True) If False, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples. sample_weight : array-like of shape = [n_samples], optional Sample weights.

Returns:

score : float If normalize == True, return the correctly classified samples (float), else it returns the number of correctly classified samples (int). The best performance is 1 with normalize == True and the number of samples with normalize == False.

See also

jaccard_similarity_score, hamming_loss, zero_one_loss

Notes

In binary and multiclass classification, this function is equal to the jaccard_similarity_score function.

Examples

1 2 3 4 5 6 7 8

>>> import numpy as np >>> from sklearn.metrics import accuracy_score >>> y_pred = [0, 2, 1, 3] >>> y_true = [0, 1, 2, 3] >>> accuracy_score(y_true, y_pred) 0.5 >>> accuracy_score(y_true, y_pred, normalize=False) 2

In the multilabel case with binary label indicators: >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2))) 0.5

Examples using sklearn.metrics.accuracy_score

Multi-class AdaBoosted Decision Trees

Probabilistic predictions with Gaussian process classification (GPC)

Classification of text documents using sparse features