Hướng dẫn dùng sklearn accuracy_score python

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.

Ground truth (correct) labels.

Predicted labels, as returned by a classifier.

If False, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples.

Sample weights.

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.

Notes

In binary classification, this function is equal to the jaccard_score function.

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

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