mars.learn.metrics.accuracy_score#

mars.learn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None, session=None, run_kwargs=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 tensor / sparse tensor) – Ground truth (correct) labels.

  • y_pred (1d array-like, or label indicator tensor / sparse tensor) – 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,), default=None) – Sample weights.

Returns

score – If normalize == True, return the fraction of correctly classified samples (float), else returns the number of correctly classified samples (int).

The best performance is 1 with normalize == True and the number of samples with normalize == False.

Return type

float

See also

jaccard_score, hamming_loss, zero_one_loss

Notes

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

Examples

>>> from mars.learn.metrics import accuracy_score
>>> y_pred = [0, 2, 1, 3]
>>> y_true = [0, 1, 2, 3]
>>> accuracy_score(y_true, y_pred).execute()
0.5
>>> accuracy_score(y_true, y_pred, normalize=False).execute()
2

In the multilabel case with binary label indicators:

>>> import mars.tensor as mt
>>> accuracy_score(mt.array([[0, 1], [1, 1]]), mt.ones((2, 2))).execute()
0.5