On Mon, Feb 27, 2012 at 6:15 PM, Olivier Grisel
<[email protected]> wrote:
> Cool, I did not know that the binary case was handled as well.
Actually most of the logic is in LabelBinarizer.
>>> from sklearn.preprocessing import LabelBinarizer
>>> lb = LabelBinarizer()
>>> lb.fit_transform([1, 2, 2, 2])
array([[ 0.],
[ 1.],
[ 1.],
[ 1.]])
>>> lb.fit_transform([1, 2, 2, 3])
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
This way, only one coefficient vector is required in the binary case.
Mathieu
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