mbrier {mlr3measures}

Multiclass Brier Score

Description

Measure to compare true observed labels with predicted probabilities in multiclass classification tasks.

Usage

mbrier(truth, prob, ...)

Arguments

truth

(factor())
True (observed) labels. Must have the same levels and length as response.

prob

(matrix())
Matrix of predicted probabilities, each column is a vector of probabilities for a specific class label. Columns must be named with levels of truth.

...

(any)
Additional arguments. Currently ignored.

Details

Brier score for multi-class classification problems with r labels defined as

\frac{1}{n} \sum_{i=1}^n \sum_{j=1}^r (I_{ij} - p_{ij})^2.

I_{ij} is 1 if observation i has true label j, and 0 otherwise.

Note that there also is the more common definition of the Brier score for binary classification problems in bbrier().

Value

Performance value as numeric(1).

Meta Information

• Type: "classif"

• Range: [0, 2]

• Minimize: TRUE

• Required prediction: prob

References

Brier GW (1950). “Verification of forecasts expressed in terms of probability.” Monthly Weather Review, 78(1), 1–3. doi:10.1175/1520-0493(1950)078<0001:vofeit>2.0.co;2.

See Also

Other Classification Measures: acc(), bacc(), ce(), logloss(), mauc_aunu(), mcc(), zero_one()

Examples

set.seed(1)
lvls = c("a", "b", "c")
truth = factor(sample(lvls, 10, replace = TRUE), levels = lvls)
prob = matrix(runif(3 * 10), ncol = 3)
colnames(prob) = levels(truth)
mbrier(truth, prob)