sklearn.metrics.top_k_accuracy_score(y_true, y_score, *, k=2, normalize=True, sample_weight=None, labels=None)[source]

Top-k Accuracy classification score.

This metric computes the number of times where the correct label is among the top k labels predicted (ranked by predicted scores). Note that the multilabel case isn’t covered here.

Read more in the User Guide

y_truearray-like of shape (n_samples,)

True labels.

y_scorearray-like of shape (n_samples,) or (n_samples, n_classes)

Target scores. These can be either probability estimates or non-thresholded decision values (as returned by decision_function on some classifiers). The binary case expects scores with shape (n_samples,) while the multiclass case expects scores with shape (n_samples, n_classes). In the nulticlass case, the order of the class scores must correspond to the order of labels, if provided, or else to the numerical or lexicographical order of the labels in y_true.

kint, default=2

Number of most likely outcomes considered to find the correct label.

normalizebool, default=True

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

sample_weightarray-like of shape (n_samples,), default=None

Sample weights. If None, all samples are given the same weight.

labelsarray-like of shape (n_classes,), default=None

Multiclass only. List of labels that index the classes in y_score. If None, the numerical or lexicographical order of the labels in y_true is used.


The top-k accuracy score. The best performance is 1 with normalize == True and the number of samples with normalize == False.

See also



In cases where two or more labels are assigned equal predicted scores, the labels with the highest indices will be chosen first. This might impact the result if the correct label falls after the threshold because of that.


>>> import numpy as np
>>> from sklearn.metrics import top_k_accuracy_score
>>> y_true = np.array([0, 1, 2, 2])
>>> y_score = np.array([[0.5, 0.2, 0.2],  # 0 is in top 2
...                     [0.3, 0.4, 0.2],  # 1 is in top 2
...                     [0.2, 0.4, 0.3],  # 2 is in top 2
...                     [0.7, 0.2, 0.1]]) # 2 isn't in top 2
>>> top_k_accuracy_score(y_true, y_score, k=2)
>>> # Not normalizing gives the number of "correctly" classified samples
>>> top_k_accuracy_score(y_true, y_score, k=2, normalize=False)