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scikit-learn 1.3.2
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# `sklearn.metrics`.mean_squared_log_error

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

  Mean squared logarithmic 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 when the input is of multioutput format.

      ‘uniform_average’ :

        Errors of all outputs are averaged with uniform weight.

    squared bool, default=True

      If True returns MSLE (mean squared log error) value. If False returns RMSLE (root mean squared log error) 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_log_error
      >>> y_true = [3, 5, 2.5, 7]
      >>> y_pred = [2.5, 5, 4, 8]
      >>> mean_squared_log_error(y_true, y_pred)
      0.039...
      >>> mean_squared_log_error(y_true, y_pred, squared=False)
      0.199...
      >>> y_true = [[0.5, 1], [1, 2], [7, 6]]
      >>> y_pred = [[0.5, 2], [1, 2.5], [8, 8]]
      >>> mean_squared_log_error(y_true, y_pred)
      0.044...
      >>> mean_squared_log_error(y_true, y_pred, multioutput='raw_values')
      array([0.00462428, 0.08377444])
      >>> mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
      0.060...