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.

BrierScore

The mean squared error of a probabilistic prediction — the gentler half of the calibration question LogLoss asks. 0 is a perfect, perfectly confident prediction, and a confident mistake costs at most 1 where the logarithm makes it unbounded.

Both are proper scoring rules: both are minimised by predicting the truth, so neither can be gamed by shading a probability toward the safer answer. What they disagree about is how much a single overconfident sample should matter. Two samples predicted 0.0 for a class that occurred score exactly 1 here and above 36 there.

scaleByHalf reads the shape, and the default follows it

The reference's scale_by_half='auto' resolves differently by input shape, and the two entry points here take that default rather than a string:

input	reference	here
one probability per sample	halves the two-class sum	Score, scaleByHalf: true
a probability matrix	does not halve	MultiClass, scaleByHalf: false

Measured on the same four-sample matrix: 0.245 unhalved and 0.1225 halved. On a binary vector: 0.0375 halved — the familiar Brier score — and 0.075 unhalved.

Members

Member	What it does
BrierScore.Score	The binary case, from one probability per sample.
BrierScore.MultiClass	The same over a probability matrix.