- 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.
- 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
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
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 == Trueand the number of samples with
normalize == False.
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.
In binary classification, this function is equal to the
>>> 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
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
Effect of varying threshold for self-training
Classification of text documents using sparse features