Mean Absolute Error

mean-absolute-error

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

When to invoke this skill

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

Reference signature

from sklearn.metrics import mean_absolute_error

# mean_absolute_error(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average')

Library docstring

Mean absolute error regression loss.

The mean absolute error is a non-negative floating point value, where best value
is 0.0. Read more in the :ref:`User Guide <mean_absolute_error>`.

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

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

sample_weight : array-like of shape (n_samples,), default=None
    Sample weights.

multioutput : {'raw_values', 'uniform_average'}  or array-like of shape             (n_outputs,), default='uniform_average'
    Defines aggregating of multiple output values.
    Array-like value defines weights used to average errors.

    'raw_values' :
        Returns a full set of errors in case of multioutput input.

    'uniform_average' :
        Errors of all outputs are averaged with uniform weight.

Returns
-------
loss : float or array of floats
    If multioutput is 'raw_values', then mean absolute error is returned
    for each output separately.
    If multioutput is 'uniform_average' or an ndarray of weights, then the
    weighted average of all output errors is returned.

    MAE output is non-negative floating point. The best value is 0.0.

Examples
--------
>>> from sklearn.metrics import mean_absolute_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_absolute_error(y_true, y_pred)
0.5
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> mean_absolute_error(y_true, y_pred)
0.75
>>> mean_absolute_error(y_true, y_pred, multioutput='raw_values')
array([0.5, 1. ])
>>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
0.85...

Quick recipe

import sklearn.metrics as _m
score = _m.mean_absolute_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).