[
https://issues.apache.org/jira/browse/SPARK-3568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Shuo Xiang updated SPARK-3568:
------------------------------
Description:
Include widely-used metrics for ranking algorithms, including:
- Mean Average Precision
- Precision@n: top-n precision
- Discounted cumulative gain (DCG) and NDCG
This implementation attempts to create a new class called `RankingMetrics`
under `org.apache.spark.mllib.evaluation`, which accepts input (prediction and
label pairs) as `RDD[Array[Double], Array[Double]]`. Methods of
`meanAveragePrecision`, `topKPrecision` and `ndcg` will be included.
was:
Include widely-used metrics for ranking algorithms, including:
- Mean Average Precision
- Precision@n: top-n precision
- Discounted cumulative gain (DCG) and NDCG
> Add metrics for ranking algorithms
> ----------------------------------
>
> Key: SPARK-3568
> URL: https://issues.apache.org/jira/browse/SPARK-3568
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib
> Reporter: Shuo Xiang
> Assignee: Shuo Xiang
> Priority: Minor
>
> Include widely-used metrics for ranking algorithms, including:
> - Mean Average Precision
> - Precision@n: top-n precision
> - Discounted cumulative gain (DCG) and NDCG
> This implementation attempts to create a new class called `RankingMetrics`
> under `org.apache.spark.mllib.evaluation`, which accepts input (prediction
> and label pairs) as `RDD[Array[Double], Array[Double]]`. Methods of
> `meanAveragePrecision`, `topKPrecision` and `ndcg` will be included.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
