On Thu, May 16, 2013 at 11:51:18PM +1000, Joel Nothman wrote:
> I notice that there are some remnants of a former scikit-learn that allowed
> parameters to fit(...) other than X and y, but these have been removed in
> favour of object-level parameters.

Any parameter that is not strong data dependent should be an object-level
parameter. Ideally, I would have like all these parameters to be length-n
vectors, in order to be able to apply cross-validation data splitting to
them, but this is maybe an over-simplification.

> copy is not really a problem. It's not something you want to vary in parameter
> search,

But it's not something that should depend on the data.

> y is the subject of the first issue I mentioned.

y seems legit. Our framework does not deal properly with it, but that's
probably a because.

> In the case of ridge_alpha, it looks like this argument should be deprecated
> (or left in as harmless?) as the code already backs off to an object property.

Yes, it should be deprecated.

> For threshold, this additional parameter also makes it harder to adopt the
> feature selection mixin. I propose that instead such estimators should have a
> to_selector/to_transformer method that returns a feature selector where a
> threshold parameter (or a lower and upper and limit threshold; see https://
> github.com/scikit-learn/scikit-learn/pull/1939) can be played with directly
> through the common scikit-learn parameters API (and therefore [grid] search).

> I haven't tried to understand pooling_func or Y so I don't get whether they're
> necessary.

> Thoughts?

The above should be object-level params, I believe.

G

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