Spherical Scoring Rule
Defined as: mean(p_i(sum_j(p_ij))), where p_i is the predicted probability of the true class of observation i and p_ij is the predicted probablity of observation i for class j. See: Bickel, J. E. (2007). Some comparisons among quadratic, spherical, and logarithmic scoring rules. Decision Analysis, 4(2), 49-65.
SSR(probabilities, truth)
probabilities |
[numeric] vector (or matrix with column names of the classes) of predicted probabilities |
truth |
vector of true values |
n = 20 set.seed(122) truth = as.factor(sample(c(1,2,3), n, replace = TRUE)) probabilities = matrix(runif(60), 20, 3) probabilities = probabilities/rowSums(probabilities) colnames(probabilities) = c(1,2,3) SSR(probabilities, truth)
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