[SPARK-1545] Add Random Forest algorithm to MLlib

• Type: New Feature
• Status: Resolved
• Priority: Major
• Resolution: Fixed
• Fix Version/s: 1.2.0
• Component/s: MLlib
• Target Version/s: 1.2.0
• Assignee: Joseph K. Bradley
• Reporter: Manish Amde
• Created: 21/Apr/14 00:16
• Updated: 29/Sep/14 04:45
• Resolved: 29/Sep/14 04:45

Description

This task requires adding Random Forest support to Spark MLlib. The implementation needs to adapt the classic algorithm to the scalable tree implementation.

The tasks involves:

• 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-1547 Add gradient boosting algorithm to MLlib
• is required by: SPARK-3703 Ensemble learning methods
• links to: [Github] Pull Request #2435 (jkbradley)