mean_absolute_percentage_error#

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

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.

Added in version 0.24.

Parameters:
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.

Returns:
lossfloat 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)
np.float64(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)
np.float64(0.5515...)
>>> mean_absolute_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7])
np.float64(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)
np.float64(112589990684262.48)
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