>
> The pipeline takes a set of transformers, and a predicter. So it should
> be able to apply the transform before the predict. I think that it should
> answer your usecase.
>
thanks! then i don't need any changes!
--
RSA
On Wed, Nov 09, 2011 at 12:02:35PM -0500, Satrajit Ghosh wrote:
> alex: regarding the pipeline, doesn't it require fit and predict
> operations. will it accept fit and transform?
The pipeline takes a set of transformers, and a predicter. So it should
be able to apply the transform before the pred
hi andreas and alex,
the basic idea is to do a transform of feature space for the training set
and apply it to the test set. since the cv routines do the split of the set
internally, there is no mechanism that i could find to do that.
alex: regarding the pipeline, doesn't it require fit and predi
hi satra,
wouldn't a pipeline solve your pb?
see eg. :
http://scikit-learn.org/stable/auto_examples/feature_selection_pipeline.html#example-feature-selection-pipeline-py
Alex
On Wed, Nov 9, 2011 at 10:55 AM, Andreas Müller
wrote:
> Hi Satra.
> Can you explain the use-case a bit more?
> I don'
Hi Satra.
Can you explain the use-case a bit more?
I don't understand it from the code, sorry.
Cheers,
Andy
On 11/09/2011 04:51 PM, Satrajit Ghosh wrote:
> i often find myself doing the following for cross-validation. i.e.
> estimating the transform from the training set. would this be useful
> as
i often find myself doing the following for cross-validation. i.e.
estimating the transform from the training set. would this be useful as a
parameter on cross_val_score, gridsearchcv, etc.,. if so i'll send a pr.
class NoTransform():
def fit(self, X):
return self
def transfor
