Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.metrics`.mean_absolute_error

sklearn.metrics.mean_absolute_error(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average')

  Mean absolute error regression loss.

  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’} 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

      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...

## Examples using `sklearn.metrics.mean_absolute_error`

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Quantile regression

Quantile regression

Tweedie regression on insurance claims

Tweedie regression on insurance claims