Scaling (or the same scaling procedure) is not always beneficial, but you
can certainly do exactly what you are saying by making a pipeline of a
StandardScaler and your estimator.
See the documentation for Pipeline at
http://scikit-learn.org/dev/modules/pipeline.html and
http://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html
and examples at
http://scikit-learn.org/dev/modules/generated/sklearn.pipeline.Pipeline.html#examples-using-sklearn-pipeline-pipeline
On 7 September 2014 07:07, Pagliari, Roberto <[email protected]>
wrote:
> Typically one should scale training data and then test data using the
> values gotten when scaling training data.
>
> When performing grid search, shouldn’t scaling occur every time (k-1)
> folds are selected as training data, and apply that to the k-th fold? So,
> in practice, everytime the training/test data change while performing
> gridsearch, scaling will be re-done as well.
>
>
>
> However, my understanding is that gridsearch does not do that in the
> process.
>
>
>
> Can someone comment on this?
>
>
>
> Thank you,
>
>
>
>
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