Metrics 0.3.0 detcurve

Lodestar.Metrics 0.3.0. This page is frozen at that release. Read the current documentation for what main says now. A link to a decision or a migration page follows main, and leaves the archive.

DetCurve

The detection error tradeoff curve: the same two errors RocCurve plots, but both as errors — false positives against false negatives rather than against true positives.

That is the whole difference, and it matters for reading a plot: on a DET curve both axes are things you want small, so a better model sits nearer the origin, where on a ROC curve a better model bows away from the diagonal.

It is the shortest of the three on the same input

Neither endpoint is carried. The curve starts where false positives stop being zero and stops where false negatives reach zero, because the region outside that is where one of the two errors is constant and the plot says nothing. Measured on the worked case: 3 points, where the ROC curve has 5 and the precision-recall curve 5.

Its points also run the other way — thresholds ascending, so the false-positive rate descends.

dropIntermediate is false here, as the reference has it, and shares PrecisionRecallCurve's rule rather than RocCurve's.

Members

Member	What it does
DetCurve.Compute	Draws the curve from labels and scores.