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https://issues.apache.org/jira/browse/SPARK-3568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Shuo Xiang updated SPARK-3568:
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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.
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