Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.metrics`.mean_squared_error

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

  Mean squared 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.

    squared bool, default=True

      If True returns MSE value, if False returns RMSE value.

  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 mean_squared_error
      >>> y_true = [3, -0.5, 2, 7]
      >>> y_pred = [2.5, 0.0, 2, 8]
      >>> mean_squared_error(y_true, y_pred)
      0.375
      >>> y_true = [3, -0.5, 2, 7]
      >>> y_pred = [2.5, 0.0, 2, 8]
      >>> mean_squared_error(y_true, y_pred, squared=False)
      0.612...
      >>> y_true = [[0.5, 1],[-1, 1],[7, -6]]
      >>> y_pred = [[0, 2],[-1, 2],[8, -5]]
      >>> mean_squared_error(y_true, y_pred)
      0.708...
      >>> mean_squared_error(y_true, y_pred, squared=False)
      0.822...
      >>> mean_squared_error(y_true, y_pred, multioutput='raw_values')
      array([0.41666667, 1.        ])
      >>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
      0.825...

## Examples using `sklearn.metrics.mean_squared_error`

Gradient Boosting regression

Gradient Boosting regression

Prediction Intervals for Gradient Boosting Regression

Prediction Intervals for Gradient Boosting Regression

Model Complexity Influence

Model Complexity Influence

Linear Regression Example

Linear Regression Example

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Quantile regression

Quantile regression

Ridge coefficients as a function of the L2 Regularization

Ridge coefficients as a function of the L2 Regularization

Robust linear estimator fitting

Robust linear estimator fitting

Tweedie regression on insurance claims

Tweedie regression on insurance claims