Github user sethah commented on the pull request:
https://github.com/apache/spark/pull/10231#issuecomment-198482885
@jkbradley I ran some local timings comparing before/after this change. I
used `RandomForestRegressor` with all continuous features. It looks like there
is a small performance impact on micro datasets, but no noticeable performance
hit on larger in-memory datasets. What do you think?
I just ran five trials each, but I can set up something more robust if
needed.
```
options = {'numRows': 10k, 'numCols': 100, 'maxDepth': 2}
with_patch without_patch
0 0.991490 0.778417
1 0.867575 0.862355
2 0.894913 0.987718
3 0.920691 0.790363
4 0.933628 0.951237
```
```
options = {'numRows': 1k, 'numCols': 10, 'maxDepth': 2}
with_patch without_patch
0 0.038660 0.015930
1 0.051568 0.015814
2 0.039481 0.018386
3 0.044415 0.016335
4 0.049889 0.017497
```
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