DOC Clarify inclusion criteria for speed ups
@@ -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 >>
+ n_samples",
+- 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
