Use the new RF backend by default for classification

[hcho3]

The new RF backend now significantly outperforms the old backend in performance, while retaining a similar model accuracy.

The default is only changed for the classifier, as the performance is still not good for the regression task.

[vinaydes]

When we flip the switch on default backend, we should change it in all unit tests, benchmark code in bench/ and elsewhere as well.

[hcho3]

Blocked by #3706

[teju85]

Changes LGTM.

[vinaydes]

Now that experimental backend is default, should we change this warning

cuml/cpp/src/decisiontree/decisiontree_impl.cuh

Line 422 in 4f40344

CUML_LOG_WARN("Using experimental backend for growing trees\n");

?

We should probably warn user when it goes back to old backend due to some limitation (quantile per tree or histogram approximation).

[hcho3]

@vinaydes I fixed all the failing tests (thanks for your suggestion). As for the warning, we should still keep it, since we are changing the default only for the classification.

[JohnZed]

Looks good! Just a tiny comment change suggested

[JohnZed]

Looks great, @hcho3 ! I made a tiny change to the comment string but looks great overall.

[JohnZed]

@gpucibot merge

[JohnZed]

@gpucibot merge

[hcho3]

rerun tests

[codecov-io]

Codecov Report

Merging #3686 (8df1942) into branch-0.19 (fd9ec89) will increase coverage by 5.24%.
The diff coverage is n/a.

❗ Current head 8df1942 differs from pull request most recent head 8b39957. Consider uploading reports for the commit 8b39957 to get more accurate results

@@               Coverage Diff               @@
##           branch-0.19    #3686      +/-   ##
===============================================
+ Coverage        80.70%   85.95%   +5.24%     
===============================================
  Files              227      225       -2     
  Lines            17615    16987     -628     
===============================================
+ Hits             14217    14601     +384     
+ Misses            3398     2386    -1012     

Flag	Coverage Δ
dask	48.96% <ø> (+3.97%)	⬆️
non-dask	77.79% <ø> (+4.88%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
python/cuml/__init__.py	95.58% <ø> (+0.20%)	⬆️
...cuml/_thirdparty/sklearn/preprocessing/__init__.py	100.00% <ø> (ø)
...on/cuml/_thirdparty/sklearn/preprocessing/_data.py	67.22% <ø> (+4.10%)	⬆️
...hirdparty/sklearn/preprocessing/_discretization.py	83.33% <ø> (-0.88%)	⬇️
...l/_thirdparty/sklearn/preprocessing/_imputation.py	85.54% <ø> (+22.74%)	⬆️
python/cuml/_thirdparty/sklearn/utils/extmath.py	97.22% <ø> (+40.32%)	⬆️
...cuml/_thirdparty/sklearn/utils/skl_dependencies.py	79.54% <ø> (+25.62%)	⬆️
...ython/cuml/_thirdparty/sklearn/utils/validation.py	64.10% <ø> (+41.65%)	⬆️
python/cuml/cluster/__init__.py	100.00% <ø> (ø)
python/cuml/cluster/agglomerative.pyx	96.47% <ø> (ø)
... and 163 more

---

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 4f40344...8b39957. Read the comment docs.