dual=True / False does not change the result, only potentially the speed of
the algorithm
With an L2 penalty, dual=True is preferable when n_samples < n_features,
dual=False (i.e. "primal=True") is preferable when n_features < n_samples
With an L1 penalty, dual=True is simply not implemented.
So, if you are searching over penalty=["L1", "L2"] and have n_samples <
n_features then you will have to provide separate dictionaries if you want
to make use of the speedup from dual=True in the L2 case.
Michael
On Tue, Nov 18, 2014 at 5:52 PM, Andy <t3k...@gmail.com> wrote:
> Hi George.
>
> A way to avoid the error is by using lists of grids, where each grid only
> has valid combinations
>
> param_grid = [{'dual': False, 'penalty': 'l1', 'C': 10. ** np.arange(-3,
> 3)}, {'dual': True, 'penalty':'l2', 'loss': 'l2', 'C': : 10. **
> np.arange(-3, 3)}]
>
> or something like that. (Look up the allowed combination in the docs, I
> don't know them by heart ;)
>
> What are you searching over? Searching over whether to use the dual or not
> does not really make much sense to me.
>
> Best,
> Andy
>
>
>
> On 11/18/2014 11:41 AM, George Bezerra wrote:
>
> I'm trying to perform a grid search cv for logistic regression but I
> stumble on invalid combination of parameters:
>
> ValueError: Unsupported set of arguments: penalty='l1' is only supported
> when dual='false'., Parameters: penalty='l1', loss='lr', dual=True
>
> Is there a way to avoid this problem?
>
> Thanks,
>
> George.
>
> --
> George Bezerra
>
>
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