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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