Can this maybe potentially addressed by feature scaling? E.g,. standardizing
all features but multiplying certain ones by a factor?
> On Jul 15, 2015, at 1:23 PM, Doaa Altarawy <daltar...@vt.edu> wrote:
>
> Hi,
>
> Is it possible some how to use the penalty term in the Elastic net (the
> alpha) as a vector instead of a scaler?
> Meaning that I want to have different penalties or shrinkage weights on each
> predictor.
>
> The purpose is to add prior knowledge where some predictors are known to
> already have a strong relation with the response, but the coefficient is not
> known.
>
> Adding different penalties was proposed in gene regulation problem is this
> paper:
> http://bioinformatics.oxfordjournals.org/content/29/8/1060.short
> <http://bioinformatics.oxfordjournals.org/content/29/8/1060.short>
>
> They called it modified Elastic net.
>
> Is there an easy way to do it in scikit-learn without having to implement it?
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