hi gael,
> In the scikit, there is a convention that everything that is a 'score' is
> 'bigger is better'. The reason is that it enables black box optimizers to
> tune parameters or select models based on this score. I wouldn't like
> to enforce that it is bound between 0 and 1 because many scores used in
> real life are not bound.
understandable. i just didn't know the convention.
> Also, in general, you cannot interpret a score (like explained_variance) as
> related to a p-value.
i don't quite understand what you mean by this. i wasn't trying to interpret
explained_variance as a p-value. that's why i was using the permutation
test, to give me a p value corresponding to my observed explained_variance
value. sorry if i wasn't clear.
> I wouldn't try to have a too simple message by fitting all the metrics in
> the framework. I don't think that it can work: they test for different
> things.
>
i think just a note to say 'bigger is better' would suffice wherever a score
func is used.
cheers,
satra
>
> Gaël
>
>
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definitive record of customers, application performance, security
threats, fraudulent activity and more. Splunk takes this data and makes
sense of it. Business sense. IT sense. Common sense.
http://p.sf.net/sfu/splunk-d2dcopy1
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