Brier score for binary classification problems defined as $$
\frac{1}{n} \sum_{i=1}^n (I_i - p_i)^2.
$$
\(I_{i}\) is 1 if observation \(i\) belongs to the positive class, and 0 otherwise.
Note that this (more common) definition of the Brier score is equivalent to the
original definition of the multi-class Brier score (see mbrier()) divided by 2.
Usage
bbrier(truth, prob, positive, ...)
Arguments
truth
(factor())
True (observed) labels.
Must have the exactly same two levels and the same length as response.
prob
(numeric())
Predicted probability for positive class.
Must have exactly same length as truth.