Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.metrics`.accuracy_score

sklearn.metrics.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.

  Read more in the User Guide.

  Parameters:
    y_true 1d array-like, or label indicator array / sparse matrix

      Ground truth (correct) labels.

    y_pred 1d array-like, or label indicator array / sparse matrix

      Predicted labels, as returned by a classifier.

    normalize bool, 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,), default=None

      Sample weights.

  Returns:
    score float

      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

  `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.

  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

## Examples using `sklearn.metrics.accuracy_score`

Plot classification probability

Plot classification probability

Multi-class AdaBoosted Decision Trees

Multi-class AdaBoosted Decision Trees

Probabilistic predictions with Gaussian process classification (GPC)

Probabilistic predictions with Gaussian process classification (GPC)

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

Importance of Feature Scaling

Importance of Feature Scaling

Effect of varying threshold for self-training

Effect of varying threshold for self-training

Classification of text documents using sparse features

Classification of text documents using sparse features