[SPARK-1548] Add Partial Random Forest algorithm to MLlib

• Type: New Feature
• Status: Resolved
• Priority: Major
• Resolution: Incomplete
• Affects Version/s: 1.0.0
• Component/s: MLlib
• Labels: bulk-closed
• Assignee: Unassigned
• Reporter: Manish Amde
• Created: 21/Apr/14 00:23
• Updated: 21/May/19 04:13
• Resolved: 21/May/19 04:13

Description

This task involves creating an alternate approximate random forest implementation where each tree is constructed per partition.

The tasks involves:

• Justifying with theory and experimental results why this algorithm is a good choice.
• Comparing the various tradeoffs and finalizing the algorithm before implementation
• Code implementation
• Unit tests
• Functional tests
• Performance tests
• Documentation

Issue Links

• is required by: SPARK-3703 Ensemble learning methods