sklearn.metrics.accuracy_score¶
- sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True)¶
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.
Parameters : y_true : array-like or list of labels or label indicator matrix
Ground truth (correct) labels.
y_pred : array-like or list of labels or label indicator 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.
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
Notes
In binary and multiclass classification, this function is equal to the jaccard_similarity_score function.
Examples
>>> 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 indicator format:
>>> accuracy_score(np.array([[0.0, 1.0], [1.0, 1.0]]), np.ones((2, 2))) 0.5
and with a list of labels format:
>>> accuracy_score([(1, ), (3, )], [(1, 2), tuple()]) 0.0