Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.metrics`.explained_variance_score

sklearn.metrics.explained_variance_score(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average', force_finite=True)

  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 \(R^2\) definition.

  Note

  The Explained Variance score is similar to the `R^2 score`, with the notable difference that it does not account for systematic offsets in the prediction. Most often the `R^2 score` should be preferred.

  Read more in the User Guide.

  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. grid search cross-validation).

      New in version 1.1.

  Returns:
    score float or ndarray of floats

      The explained variance or ndarray if ‘multioutput’ is ‘raw_values’.

  See also

  `r2_score`

    Similar metric, but accounting for systematic offsets in prediction.

  Notes

  This is not a symmetric function.

  Examples

      >>> from sklearn.metrics import explained_variance_score
      >>> y_true = [3, -0.5, 2, 7]
      >>> y_pred = [2.5, 0.0, 2, 8]
      >>> explained_variance_score(y_true, y_pred)
      0.957...
      >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
      >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
      >>> explained_variance_score(y_true, y_pred, multioutput='uniform_average')
      0.983...
      >>> y_true = [-2, -2, -2]
      >>> y_pred = [-2, -2, -2]
      >>> explained_variance_score(y_true, y_pred)
      1.0
      >>> explained_variance_score(y_true, y_pred, force_finite=False)
      nan
      >>> y_true = [-2, -2, -2]
      >>> y_pred = [-2, -2, -2 + 1e-8]
      >>> explained_variance_score(y_true, y_pred)
      0.0
      >>> explained_variance_score(y_true, y_pred, force_finite=False)
      -inf