Hi,
BinaryClassificationMetrics expose recall and precision byThreshold. Is there a
way to true negatives / false negatives etc per threshold?
I have weighted my genuines and would like the adjusted precision / FPR.
(Unless there is an option that I've missed, although I have been over the
Class twice now and can't see any weighting options). I had to build my own,
which seems a bit like reinventing the wheel (isn't as safe + fast for a start):
val threshold_stats =
metrics.thresholds.cartesian(predictionAndLabels).map{case (t, (prob, label)) =>
val selected = (prob >= t)
val fraud = (label == 1.0)
val tp = if (fraud && selected) 1 else 0
val fp = if (!fraud && selected) 1 else 0
val tn = if (!fraud && !selected) 1 else 0
val fn = if (fraud && !selected) 1 else 0
(t, (tp, fp, tn, fn))
}.reduceByKey((x, y) => (x._1 + y._1, x._2 + y._2, x._3 + y._3, x._4 + y._4))
Kind Regards,
Alex.
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