[SPARK-3703] Ensemble learning methods

• Type: Improvement
• Status: Resolved
• Priority: Major
• Resolution: Incomplete
• Component/s: MLlib
• Labels: bulk-closed
• Assignee: Unassigned Assign to me
• Reporter: Joseph K. Bradley
• Created: 26/Sep/14 22:06
• Updated: 21/May/19 05:36
• Resolved: 21/May/19 05:36

Description

This is a general JIRA for coordinating on adding ensemble learning methods to MLlib. These methods include a variety of boosting and bagging algorithms. Below is a general design doc for ensemble methods (currently focused on boosting). Please comment here about general discussion and coordination; for comments about specific algorithms, please comment on their respective JIRAs.

Design doc for ensemble methods

Issue Links

• requires: SPARK-1545 Add Random Forest algorithm to MLlib
• requires: SPARK-1546 Add AdaBoost algorithm to Spark MLlib
• requires: SPARK-1547 Add gradient boosting algorithm to MLlib
• requires: SPARK-1548 Add Partial Random Forest algorithm to MLlib
• requires: SPARK-7129 Add generic boosting algorithm to spark.ml
• requires: SPARK-2401 AdaBoost.MH, a multi-class multi-label classifier
• requires: SPARK-7128 Add generic bagging algorithm to spark.ml