Explained Variance Score

explained-variance-score

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

When to invoke this skill

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

Reference signature

from sklearn.metrics import explained_variance_score

# explained_variance_score(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average', force_finite=True)

Library docstring

Explained variance regression score function.

Best possible score is 1.0, lower values are worse.

In the particular case when ``y_true`` is constant, the explained variance
score is not finite: it is either ``NaN`` (perfect predictions) or
``-Inf`` (imperfect predictions). To prevent such non-finite numbers to
pollute higher-level experiments such as a grid search cross-validation,
by default these cases are replaced with 1.0 (perfect predictions) or 0.0
(imperfect predictions) respectively. If ``force_finite``
is set to ``False``, this score falls back on the original :math:`R^2`
definition.

.. note::
   The Explained Variance score is similar to the :func:`R^2 score <r2_score>`,
   but the former does not account for systematic offsets in the prediction
   (such as the intercept in linear models, i.e. different intercepts give
   the same Explained Variance score). Most often the :func:`R^2 score
   <r2_score>` should be preferred.

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

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', 'variance_weighted'} or             array-like of shape (n_outputs,), default='uniform_average'
    Defines aggregating of multiple output scores.
    Array-like value defines weights used to average scores.

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

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

    'variance_weighted' :
        Scores of all outputs are averaged, weighted by the variances
        of each individual output.

force_finite : bool, default=True
    Flag indicating if ``NaN`` and ``-Inf`` scores resulting from constant
    data should be replaced with real numbers (``1.0`` if prediction is
    perfect, ``0.0`` otherwise). Default is ``True``, a convenient setting
    for hyperparameters' search procedures (e.g.

Quick recipe

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