[SPARK-14409] Investigate adding a RankingEvaluator to ML

• Type: New Feature
• Status: Resolved
• Priority: Minor
• Resolution: Duplicate
• Component/s: ML
• Assignee: Unassigned
• Reporter: Nicholas Pentreath
• Shepherd: Nicholas Pentreath
• Created: 05/Apr/16 18:50
• Updated: 03/Oct/19 15:51
• Resolved: 19/Jun/19 13:22

Description

mllib.evaluation contains a RankingMetrics class, while there is no RankingEvaluator in ml.evaluation. Such an evaluator can be useful for recommendation evaluation (and can be useful in other settings potentially).

Should be thought about in conjunction with adding the "recommendAll" methods in SPARK-13857, so that top-k ranking metrics can be used in cross-validators.

Issue Links

• duplicates: SPARK-28045 add missing RankingEvaluator
• is related to: SPARK-13857 Feature parity for ALS ML with MLLIB
• is related to: SPARK-18948 Add Mean Percentile Rank metric for ranking algorithms
• relates to: SPARK-14489 RegressionEvaluator returns NaN for ALS in Spark ml
• links to: [Github] Pull Request #12461 (yongtang)
• links to: [Github] Pull Request #16618 (daniloascione)
• links to: GitHub Pull Request #12461
• links to: GitHub Pull Request #16618