accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)¶
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.
For SnapML solver this supports both local and distributed(MPI) method of execution.
Read more in the User Guide.
- y_true (1d array-like, or label indicator array / sparse matrix) – Ground truth (correct) labels. It also accepts SnapML data partition, which includes the 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.
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 == Trueand the number of samples with
normalize == False.
In binary and multiclass classification, this function is equal to the
>>> import numpy as np >>> from pai4sk.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