Hi Partrick.
The reason we don't support warm starts for LogisticRegression is that
we are using LibLinear and no-one was in the mood to hack on that yet ;)
It is a bit non-trivial C-hackery I am afraid.
Using Mathieu's library is definitely a good option.
Cheers,
Andy
On 05/17/2013 12:13 AM, Patrick Mineault wrote:
Hi all,
I'm performing logistic regression with an L1 penalty with
sklearn.linear_model.LogisticRegression. I'd like to get the entire
regularization path (from highly regularized to not regularized),
similar to lasso_path or lars_path.
However, I've verified that if you call train a second time on the
same object/same data, it takes the same amount of time to perform the
training, which mains that the class doesn't use previous estimates of
coef_ - it doesn't use a warm-start strategy.
Warm-start would be a lot faster - is there any way of doing
warm-start with LogisticRegression, or some workaround to get the
entire regularization path for logistic regression with an L1 penalty?
Patrick Mineault
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