Switching the joblib backend for UpliftRandomForestClassifier to threading

[jeongyoonlee]

Proposed changes

This PR fixes #442. By switching the joblib backend from default (locky) to threading, it uses shared memory and doesn't copy data across trees when n_jobs > 1.

References

• joblib's documentation
• Scikit-Learn's tree parallelization code

Types of changes

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• Bugfix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Documentation Update (if none of the other choices apply)

Checklist

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• Lint and unit tests pass locally with my changes
• I have added tests that prove my fix is effective or that my feature works
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Further comments

N/A.

[paullo0106]

Thank Jeong!