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
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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