Top K Accuracy Score

top-k-accuracy-score

Metric top_k_accuracy_score from scikit-learn (sklearn.metrics.top_k_accuracy_score)

When to invoke this skill

The user has predictions + ground truth and asks to evaluate with top_k_accuracy_score, or mentions sklearn.metrics.top_k_accuracy_score directly, or wants the standard scikit-learn implementation.

Reference signature

from sklearn.metrics import top_k_accuracy_score

# top_k_accuracy_score(y_true, y_score, *, k=2, normalize=True, sample_weight=None, labels=None)

Library docstring

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 :ref:`User Guide <top_k_accuracy_score>`

Parameters
----------
y_true : array-like of shape (n_samples,)
    True labels.

y_score : array-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
    :term:`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 multiclass 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``.
    If ``y_true`` does not contain all the labels, ``labels`` must be
    provided.

k : int, default=2
    Number of most likely outcomes considered to find the correct label.

normalize : bool, default=True
    If `True`, return the fraction of correctly classified samples.
    Otherwise, return the number of correctly classified samples.

sample_weight : array-like of shape (n_samples,), default=None
    Sample weights. If `None`, all samples are given the same weight.

labels : array-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. If ``y_true`` does not contain all the labels,
    ``labels`` must be provided.

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

See Also
--------
accuracy_score : Compute the accuracy score. By default, the function will
    return the fraction of correct predictions divided by the total number
    of predictions.

Notes
-----
In cases where two or more labels are assigned equal predicted scores,
the labels with the highest

Quick recipe

import sklearn.metrics as _m
score = _m.top_k_accuracy_score(y_true, y_pred)

Don'ts

• Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
• Always check the library version's argument order — sklearn is (y_true, y_pred) while torchmetrics is (preds, target).