accuracy_score

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. Sparse matrix is only supported when labels are of multilabel type.

y_pred1d array-like, or label indicator array / sparse matrix

Predicted labels, as returned by a classifier. Sparse matrix is only supported when labels are of multilabel type.

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

If normalize == True, returns the fraction of correctly classified samples, else returns the number of correctly classified samples.

The best performance is 1.0 with normalize == True and the number of samples with normalize == False.

See also

balanced_accuracy_score

Compute the balanced accuracy to deal with imbalanced datasets.

jaccard_score

Compute the Jaccard similarity coefficient score.

hamming_loss

Compute the average Hamming loss or Hamming distance between two sets of samples.

zero_one_loss

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

Gallery examples

Plot classification probability

Multi-class AdaBoosted Decision Trees

Probabilistic predictions with Gaussian process classification (GPC)

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

Importance of Feature Scaling

Release Highlights for scikit-learn 1.9

Effect of varying threshold for self-training

Classification of text documents using sparse features