accuracy_score (original) (raw)
sklearn.metrics.accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=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_true1d array-like, or label indicator array / sparse matrix
Ground truth (correct) labels.
y_pred1d array-like, or label indicator array / sparse matrix
Predicted labels, as returned by a classifier.
normalizebool, default=True
If False, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
Returns:
scorefloat or int
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.
See also
Compute the balanced accuracy to deal with imbalanced datasets.
Compute the Jaccard similarity coefficient score.
Compute the average Hamming loss or Hamming distance between two sets of samples.
Compute the Zero-one classification loss. By default, the function will return the percentage of imperfectly predicted subsets.
Examples
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) 2.0
In the multilabel case with binary label indicators:
import numpy as np accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2))) 0.5