Root Mean Squared Error

root-mean-squared-error

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

When to invoke this skill

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

Reference signature

from sklearn.metrics import root_mean_squared_error

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

Library docstring

Root mean squared error regression loss.

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

.. versionadded:: 1.4

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 ndarray of floats
    A non-negative floating point value (the best value is 0.0), or an
    array of floating point values, one for each individual target.

Examples
--------
>>> from sklearn.metrics import root_mean_squared_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> root_mean_squared_error(y_true, y_pred)
0.612...
>>> y_true = [[0.5, 1],[-1, 1],[7, -6]]
>>> y_pred = [[0, 2],[-1, 2],[8, -5]]
>>> root_mean_squared_error(y_true, y_pred)
0.822...

Quick recipe

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