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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:
------------------------------
    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]]*. The following methods will 
be implemented:

 - *averagePrecision(val position: Int): Double* this is the presicion@n
 - *meanAveragePrecision*: the average of precision@n for all values of n
- *ndcg*



  was:
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.


> 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]]*. The following 
> methods will be implemented:
>  - *averagePrecision(val position: Int): Double* this is the presicion@n
>  - *meanAveragePrecision*: the average of precision@n for all values of n
> - *ndcg*



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