sklearn.metrics.mean_absolute_percentage_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average')[source]

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.

y_truearray-like of shape (n_samples,) or (n_samples, n_outputs)

Ground truth (correct) target values.

y_predarray-like of shape (n_samples,) or (n_samples, n_outputs)

Estimated target values.

sample_weightarray-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.

lossfloat or ndarray of floats in the range [0, 1/eps]

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.


>>> 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)
>>> 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)
>>> mean_absolute_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7])