Thanks Olivier. But I want to ask a question about this. Isn't it a problem
that we give such a big steps?
I worked on Weka a bit more and it seems that Logistic Regression in scikit
is similar to Weka's LibLINEAR. I tried them on the same dataset. Results
are similar except especially *accommodate*. I gave the same parameter
values (*C = 191, tol=0.0001, L2-regularized logistic regression, no
randomization, no normalization etc..*) to both classifiers and Weka gives
me 0.58, but LR in scikit gives me 0.46.
I am tired a bit now :)
On Thu, Jan 24, 2013 at 9:51 PM, Olivier Grisel <olivier.gri...@ensta.org>wrote:
> You could speed up your grid search greatly by using an exponential
> scale for the values of C:
>
> import numpy as np
> parameters = {"C": np.logspace(0, 4, 5)}
>
>
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Osman Başkaya
Koc University
MS Student | Computer Science and Engineering
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