On Thu, Feb 19, 2015 at 11:59 PM, Matthew Lardy <[email protected]> wrote:
>
> I have been able to build models via scikit-learn with the RDKit python
> wrappers. That all works beautifully!
>
It's a nice combination, isn't it?
> What I am struggling to get are the weights, or scalers, applied to each
> bit position. For a SVM regression model (SVR) I think that the values I
> seek are in the coef_ (if the model is created via the linear kernel).
> But, all I get is something like this when I print that out:
>
> [[-0. -0.87146158 -0.46331996 ..., 0.31076767 -0.
> -0.81882195]]
>
>
I don't really know the SVM regression approach particularly well, but it
looks like that's a vector of vectors. Is the length of the inner vector
the same as the length of the fingerprint/descriptor vector you are
providing?
-greg
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