scikit-learn/scikit-learn — comment on commit b3839837cf949db8e9122807578ebce6bf3d6fb4
File:  line 0
By GaelVaroquaux:

> Maybe we should revert C to be unscaled

That's simply meaningless: it breaks cross-validation where the number of
samples vary accross folds, or between parameter tuning and training the
final model.

> and add an scaled alpha parameter which is None by zero:

In the long run, I'd like to have a param, something like 'adim_params'
that converts all constants to adimensional value. If true, C would be
divided by C_min in SVMs/logistic regression. In lasso alpha would be
divided by alpha_max. In general we should try and find as much as
possible a way to make it so that the different parameters are not
modified by some trivial change such as sample size or regressor scaling.

G