@amueller, I'd like to work on this.

sure, go for it.

@amueller, all sklearn metrics have an output in the[0,1] interval. Is it okay that this metric will have its output in [0,100]? or should we not multiply with 100 and only keep the fraction?

@mohamed-ali not all. MSE doesn't, for example (though I guess all with a fixed range do). I think we should multiply by 100 given that it's part of the definition, even though it's slightly different from other metrics.

@amueller okay great. Thanks for the quick reply :)

@amueller regarding the scorer, based on the deprecation message in the following example, I assume there should be a neg_mean_absolute_percentage_error_scorer instead of mean_absolute_percentage_error_scorer:

neg_mean_squared_error_scorer = make_scorer(mean_squared_error,
                                            greater_is_better=False)
deprecation_msg = ('Scoring method mean_squared_error was renamed to '
                   'neg_mean_squared_error in version 0.18 and will '
                   'be removed in 0.20.')
mean_squared_error_scorer = make_scorer(mean_squared_error,
greater_is_better=False)

https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/scorer.py#L472-L497

Is my assumption correct?

@amueller & @jnothman, I pushed the work in progress at this pull request #10711. We can move the discussion there and probably vote with the core devs on right name to consider.

@amueller & @jnothman, the work is done in #10711. Could you please do a full review?

Thanks.

@amueller @jnothman, out of curiosity, why do we prefix the scorer name with neg_?

@jnothman thank you!