[SPARK-7129] Add generic boosting algorithm to spark.ml

• Type: New Feature
• Status: Resolved
• Priority: Major
• Resolution: Incomplete
• Component/s: ML
• Labels: bulk-closed
• Assignee: Unassigned
• Reporter: Joseph K. Bradley
• Created: 24/Apr/15 19:17
• Updated: 21/May/19 04:12
• Resolved: 21/May/19 04:12

Description

The Pipelines API will make it easier to create a generic Boosting algorithm which can work with any Classifier or Regressor. Creating this feature will require researching the possible variants and extensions of boosting which we may want to support now and/or in the future, and planning an API which will be properly extensible.

In particular, it will be important to think about supporting:

• multiple loss functions (for AdaBoost, LogitBoost, gradient boosting, etc.)
• multiclass variants
• multilabel variants (which will probably be in a separate class and JIRA)
• For more esoteric variants, we should consider them but not design too much around them: totally corrective boosting, cascaded models

Note: This may interact some with the existing tree ensemble methods, but it should be largely separate since the tree ensemble APIs and implementations are specialized for trees.

Issue Links

• is required by: SPARK-3703 Ensemble learning methods
• relates to: SPARK-7409 Designing multilabel abstractions for spark.ml