Thanks for your answer.
Just to clarify, you are saying that there is no procedure in scikit that
will transform categorical feature values into numerical values like I was
trying to do here. Correct?
Anne Dwyer
On Wed, Mar 27, 2013 at 4:05 PM, Lars Buitinck <[email protected]> wrote:
> 2013/3/27 Anne Dwyer <[email protected]>:
> > I'm trying to convert categorical data to input data for an SVM. I am
> trying
> > to transform the data first to label encoded data, then use the one hot
> > encoding procedure.
> >
> > Can some please explain what I am doing wrong?
>
> LabelEncoder is for class labels, not features. If you do want to use
> it, you need one encoder per column, but you're probably better off
> using numpy.unique with return_inverse=True (again, once per column)
> followed by a OneHotEncoder.
>
> You can also use a DictVectorizer if you don't mind getting some
> superfluous columns.
>
> --
> Lars Buitinck
> Scientific programmer, ILPS
> University of Amsterdam
>
>
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