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DOC: Inclusion criteria for speed ups #13255

merged 3 commits into from Feb 25, 2019
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Just for now

@@ -116,6 +116,15 @@ improvements, if any, with benchmarks and/or plots. It is expected that the
proposed algorithm should outperform the methods that are already implemented
in scikit-learn at least in some areas.

Inclusion of a new algorithm speeding up an existing model is easier if:

- it does not introduce new hyper-parameters (as it makes the library
more future-proof),
- it is easy to document clearly when the contribution improves the speed
and when it does not, for instance "when n_features >>
- benchmarks clearly show a speed up.

Also note that your implementation need not be in scikit-learn to be used
together with scikit-learn tools. You can implement your favorite algorithm
in a scikit-learn compatible way, upload it to GitHub and let us know. We
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