sklearn.metrics.mean_absolute_percentage_error
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sklearn.metrics.mean_absolute_percentage_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average')
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Mean absolute percentage error regression loss.
Note here that we do not represent the output as a percentage in range [0, 100]. Instead, we represent it in range [0, 1/eps]. Read more in the User Guide.
New in version 0.24.
- Parameters
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y_truearray-like of shape (n_samples,) or (n_samples, n_outputs)
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Ground truth (correct) target values.
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y_predarray-like of shape (n_samples,) or (n_samples, n_outputs)
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Estimated target values.
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sample_weightarray-like of shape (n_samples,), default=None
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Sample weights.
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multioutput{‘raw_values’, ‘uniform_average’} or array-like
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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’ :
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Returns a full set of errors in case of multioutput input.
- ‘uniform_average’ :
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Errors of all outputs are averaged with uniform weight.
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- Returns
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lossfloat or ndarray of floats in the range [0, 1/eps]
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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 the fact that bad predictions can lead to arbitarily 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.
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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...
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Licensed under the 3-clause BSD License.
https://scikit-learn.org/0.24/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html