Restrict hyperparameter choice based on data format about hyperopt-sklearn HOT 1 OPEN

This is a good question...

You know which pre-processing pipelines yield sparse features right?

How about making a choice between
(a) a sparse-output pipeline + a reasonable classifier for sparse data
(b) a dense-output pipeline + classif for dense data

On Tue, May 6, 2014 at 12:47 PM, Brent Komer [email protected]:

For example, KNN has a distance metric parameter and some metrics cannot
be used on sparse data (e.g. chebyshev). Need a nice way to prevent these
from being selected when sparse data is used.

One way could be to have a separate search space defined for sparse and
dense data. (i.e. knn() and knn_sparse())
Another option could be to have a sparse/dense flag that changes how the
space is defined (i.e. knn( sparse=True ))

I'm leaning towards the second option.

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