measures (version 0.2)

Brier: Brier score

Description

The Brier score is defined as the quadratic difference between the probability and the value (1,0) for the class. That means we use the numeric representation 1 and 0 for our target classes. It is similiar to the mean squared error in regression. multiclass.brier is the sum over all one vs. all comparisons and for a binary classifcation 2 * brier.

Usage

Brier(probabilities, truth, negative, positive)

Arguments

probabilities

[numeric] vector of predicted probabilities

truth

vector of true values

negative

negative class

positive

positive class

Examples

# NOT RUN {
n = 20
set.seed(125)
truth = as.factor(sample(c(1,0), n, replace = TRUE))
probabilities = runif(n)
response = as.factor(as.numeric(probabilities > 0.5))
positive = 1
negative = 0
Brier(probabilities, truth, negative, positive)
# }