zhengruifeng created SPARK-13435: ------------------------------------ Summary: 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
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. -- 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