zhengruifeng created SPARK-13435:
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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.
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