dask_ml.metrics.mean_absolute_percentage_error(y_true: ArrayLike, y_pred: ArrayLike, sample_weight: Optional[ArrayLike] = None, multioutput: Optional[str] = 'uniform_average', compute: bool = True) → ArrayLike
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. compute : bool Whether to compute this result (default True) loss : float or array-like 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 None, then the equally-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.