2013-07-30 23:14:15 UTC
I'm sure you're all burnt out from what looks like a great sprint; thanks
for all that work and congratulations on the RC! So I apologise for the bad
I am wondering why there is a need to support the indices=False case in
cross_validation. Indices are superior in that they can be used with
np.take and with sparse matrices. And most of the standard cv
implementations output indices that are converted into boolean masks and
back to indices.
Moreover, building generic tools that take cv implementations as input need
to handle both cases (or make assumptions).
What is the intention behind indices=False; why not deprecate it and
simplify the API and code? (And speed up indexing by using np.take.)