Max Error

max-error

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

When to invoke this skill

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

Reference signature

from sklearn.metrics import max_error

# max_error(y_true, y_pred)

Library docstring

The max_error metric calculates the maximum residual error.

Read more in the :ref:`User Guide <max_error>`.

Parameters
----------
y_true : array-like of shape (n_samples,)
    Ground truth (correct) target values.

y_pred : array-like of shape (n_samples,)
    Estimated target values.

Returns
-------
max_error : float
    A positive floating point value (the best value is 0.0).

Examples
--------
>>> from sklearn.metrics import max_error
>>> y_true = [3, 2, 7, 1]
>>> y_pred = [4, 2, 7, 1]
>>> max_error(y_true, y_pred)
1.0

Quick recipe

import sklearn.metrics as _m
score = _m.max_error(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).