see
https://github.com/scikit-learn/scikit-learn/pulls?utf8=%E2%9C%93&q=is%3Apr+is%3Aclosed+scale_C+
for historical perspective on this issue.
Alex
On Wed, May 29, 2019 at 11:32 PM Stuart Reynolds
wrote:
>
> I looked into like a while ago. There were differences in which algorithms
> regulariz
I looked into like a while ago. There were differences in which algorithms
regularized the intercept, and which ones do not. (I believe liblinear
does, lbgfs does not).
All of the algorithms disagreed with logistic regression in scipy.
- Stuart
On Wed, May 29, 2019 at 10:50 AM Andreas Mueller wr
That is not very ideal indeed.
I think we just went with what liblinear did, and when saga was
introduced kept that behavior.
It should probably be scaled as in Lasso, I would imagine?
On 5/29/19 1:42 PM, Michael Eickenberg wrote:
Hi Jesse,
I think there was an effort to compare normalizatio
Hi Jesse,
I think there was an effort to compare normalization methods on the data
attachment term between Lasso and Ridge regression back in 2012/13, but
this might have not been finished or extended to Logistic Regression.
If it is not documented well, it could definitely benefit from a
documen
Hi everyone,
I noticed recently that in the Lasso implementation (and docs), the MSE
term is normalized by the number of samples
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html
but for LogisticRegression + L1, the logloss does not seem to be normalized
by the num
