[jira] [Commented] (SPARK-13435) Add Weighted Cohen's kappa to MulticlassMetrics

["zhengruifeng (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-13435?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15794740#comment-15794740 ] 

zhengruifeng commented on SPARK-13435:
--------------------------------------

I think this is too out of date, so I will close it. 

> Add Weighted Cohen's kappa to MulticlassMetrics
> -----------------------------------------------
>
>                 Key: SPARK-13435
>                 URL: https://issues.apache.org/jira/browse/SPARK-13435
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>            Reporter: zhengruifeng
>            Priority: Minor
>
> Add the missing Weighted Cohen's kappa to MulticlassMetrics.
> Kappa is widely used in Competition and Statistics.
> https://en.wikipedia.org/wiki/Cohen's_kappa
> Some usage examples:
> val metrics = new MulticlassMetrics(predictionAndLabels)
> // The default kappa value (Unweighted kappa)
> val kappa = metrics.kappa
> // Three built-in weighting type ("default":unweighted, "linear":linear weighted, "quadratic":quadratic weighted)
> val kappa = metrics.kappa("quadratic")
> // User-defined weighting matrix
> val matrix = Matrices.dense(n, n, values)
> val kappa = metrics.kappa(matrix)
> // User-defined weighting function
> def getWeight(i: Int, j:Int):Double = {
>           if (i == j) {
>             0.0
>           } else {
>             1.0
>           }
> }
> val kappa = metrics.kappa(getWeight) // equals to the unweighted kappa
> The calculation correctness was tested on several small data, and compared to two python's package:  sklearn and ml_metrics.



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