mars.learn.metrics.
accuracy_score
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.
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.
False
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.
score – If normalize == True, return the fraction of correctly classified samples (float), else returns the number of correctly classified samples (int).
normalize == True
The best performance is 1 with normalize == True and the number of samples with normalize == False.
normalize == False
float
See also
jaccard_score, hamming_loss, zero_one_loss
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