On Sun, Mar 18, 2012 at 4:45 AM, James Bergstra
wrote:
> I agree that it's a good idea to correct C for sample size when moving
> from a sub-problem to the full thing. I just wouldn't use the word
> "optimal" to describe the new value of C that you get this way - it's
> an extrapolation, a good
On Sat, Mar 17, 2012 at 1:51 PM, Alexandre Gramfort
wrote:
>> This statement doesn't sound true. Generally hyper-parameters
>> (especially ones to do with regularization) *do* depend on training
>> set size, and not in such straightforward ways. Data is never
>> perfectly I.I.D. and sometimes it
> This statement doesn't sound true. Generally hyper-parameters
> (especially ones to do with regularization) *do* depend on training
> set size, and not in such straightforward ways. Data is never
> perfectly I.I.D. and sometimes it can be far from it. My impression
> was that standard practice f
On 03/07/2012 11:18 AM, Alexandre Gramfort wrote:
>> I love that :)
>> Then I can finally put my MLP code somewhere ;)
>>
> give it a start then.
>
> the convention should be that the gist contains 1 file with an "if
> __name__ == '__main__':"
> that contains an example that people can try. I
On Sat, Mar 17, 2012 at 4:44 AM, Alexandre Gramfort
wrote:
> without the scale_C the libsvm/liblinear bindings are the only models
> whose hyperparameters
> depend on the training set size.
This statement doesn't sound true. Generally hyper-parameters
(especially ones to do with regularization) *
Op 17 maart 2012 13:25 heeft Conrad Lee het
volgende geschreven:
> The google prediction API seems to do some of this automatic detection of
> whether a feature is categorical or numerical. For example, if at least one
> value of a feature is a string, then they treat that feature as categorical.
>
> > We could try to create a function that takes an arbitrary matrix of
> feature
> > vectors, and automatically converts the fields that appear to be
> categorical
> > into boolean fields. Of course, we won't be able to write a function
> that
> > always knows which fields are categorical and
hi guys,
the scale_C is not released yet and not setting it in the current release raises
a warning. But maybe we could be even more explicit to warn users.
right now C is None by default and defaults to n_samples which amounts
to the C=1 with scale_C=False which is the default behavior of libsvm
