Given recall by threshold, you can compute true positive count per
threshold by just multiplying through by the count of elements where
label = 1. From that you can get false negatives by subtracting from
that same count.
Given precision by threshold, and true positives count by threshold,
you can work out how the count of elements that were predicted as
positive, by threshold. That should then get you to false positive
count, and from there you can similarly work out false negatives.
So I think you can reverse engineer those stats from precision and
recall without much trouble. I don't think there's a more direct way,
no, unless you want to clone the code and/or hack into it with
reflection.
On Fri, Sep 2, 2016 at 2:54 PM, Spencer, Alex (Santander)
<alex.spen...@santander.co.uk.invalid> wrote:
> 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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