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https://issues.apache.org/jira/browse/SPARK-13435?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15157000#comment-15157000
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Apache Spark commented on SPARK-13435:
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User 'zhengruifeng' has created a pull request for this issue:
https://github.com/apache/spark/pull/11303
> 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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