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

## 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`

.- positive
(

`character(1))`

Name of the positive class.- sample_weights
(

`numeric()`

)

Vector of non-negative and finite sample weights. Must have the same length as`truth`

. The vector gets automatically normalized to sum to one. Defaults to equal sample weights.- ...
(

`any`

)

Additional arguments. Currently ignored.

## Details

The Binary Brier Score is defined as $$ \frac{1}{n} \sum_{i=1}^n w_i (I_i - p_i)^2. $$

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.

## References

https://en.wikipedia.org/wiki/Brier_score

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
.