[BUG] Random Forest Cython API and related refactoring

[vinaydes]

Describe the bug We should consider following refactorings in the RF Cython and related C++ code.

1. Break RF parameters in to two categories:
   • Strictly RF parameters: Parameters that are consistent with scikit-learn RF parameters. Ex max_features, max_depth etc.
   • Implementation/tuning parameters: Parameters that tweak implementation, ex. split_algo, max_batch_size. A user should be required to only specify RF parameters. The implementation should try to choose best possible values for the tuning parameters for given problem. However if advanced user want to try different things by tweaking implementation parameters, there would still be a way. Typical code with this change look like

...
clf =  cuml.ensemble.RandomForestClassifier(... RF parameters ...)
# Optional
clf.set_impl_params(... Implmenetation parameters ...)
clf.fit(X, y)
...

2. Too many APIs for setting RF and decision tree parameters:

   • set_rf_params
   • set_all_rf_params
   • set_rf_class_obj
   • set_tree_params Ideally only two should be enough one for RF and other for decision tree.

3. The DecisionTreeParams struct in randomforest_shared.pxd link is incomplete and unused. May be it can be removed?

4. The _params_names list is incomplete link. Is it possible to add a automatic change that would capture?

5. Code duplication: Almost everything is duplicated between randomforest_classifier.pyx and randomforest_regressor.pyx

Anything more that might come up after we take a relook at Cython code for RF.

[vinaydes]

@venkywonka Would be taking look at the issues reported here.

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[venkywonka]

Work items yet to be done:

• [x] complete the __params_names list
• [ ] fuse the code duplication in randomforest_classifier.pyx and randomforest_regressor.pyx
• [ ] separate API for cuml-only implementation params