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# `sklearn.metrics`.mean_absolute_percentage_error

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

  Mean absolute percentage error (MAPE) regression loss.

  Note here that the output is not a percentage in the range [0, 100] and a value of 100 does not mean 100% but 1e2. Furthermore, the output can be arbitrarily high when `y_true` is small (which is specific to the metric) or when `abs(y_true - y_pred)` is large (which is common for most regression metrics). Read more in the User Guide.

  New in version 0.24.

  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

      Defines aggregating of multiple output values. Array-like value defines weights used to average errors. If input is list then the shape must be (n_outputs,).

      ‘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 percentage 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.

      MAPE output is non-negative floating point. The best value is 0.0. But note that bad predictions can lead to arbitrarily large MAPE values, especially if some `y_true` values are very close to zero. Note that we return a large value instead of `inf` when `y_true` is zero.

  Examples

      >>> from sklearn.metrics import mean_absolute_percentage_error
      >>> y_true = [3, -0.5, 2, 7]
      >>> y_pred = [2.5, 0.0, 2, 8]
      >>> mean_absolute_percentage_error(y_true, y_pred)
      0.3273...
      >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
      >>> y_pred = [[0, 2], [-1, 2], [8, -5]]
      >>> mean_absolute_percentage_error(y_true, y_pred)
      0.5515...
      >>> mean_absolute_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7])
      0.6198...
      >>> # the value when some element of the y_true is zero is arbitrarily high because
      >>> # of the division by epsilon
      >>> y_true = [1., 0., 2.4, 7.]
      >>> y_pred = [1.2, 0.1, 2.4, 8.]
      >>> mean_absolute_percentage_error(y_true, y_pred)
      112589990684262.48