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 withnormalize == False
.- Return type
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