sklearn.metrics
.top_k_accuracy_score¶

sklearn.metrics.
top_k_accuracy_score
(y_true, y_score, *, k=2, normalize=True, sample_weight=None, labels=None)[source]¶ Topk 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
 Parameters
 y_truearraylike of shape (n_samples,)
True labels.
 y_scorearraylike of shape (n_samples,) or (n_samples, n_classes)
Target scores. These can be either probability estimates or nonthresholded 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 iny_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_weightarraylike of shape (n_samples,), default=None
Sample weights. If
None
, all samples are given the same weight. labelsarraylike of shape (n_classes,), default=None
Multiclass only. List of labels that index the classes in
y_score
. IfNone
, the numerical or lexicographical order of the labels iny_true
is used.
 Returns
 scorefloat
The topk accuracy score. The best performance is 1 with
normalize == True
and the number of samples withnormalize == False
.
See also
Notes
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.
Examples
>>> 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) 0.75 >>> # Not normalizing gives the number of "correctly" classified samples >>> top_k_accuracy_score(y_true, y_score, k=2, normalize=False) 3