Log score for nominal outcomes
The Log Score is the negative logarithm of the probability assigned to the observed value. It is a proper scoring rule. Small values are better (best is zero, worst is infinity).
Arguments
- observed
A factor of length n with N levels holding the observed values.
- predicted
nxN matrix of predictive probabilities, n (number of rows) being the number of observations and N (number of columns) the number of possible outcomes.
- predicted_label
A factor of length N, denoting the outcome that the probabilities in
predicted
correspond to.
See also
Other log score functions:
logs_sample()
,
scoring-functions-binary
Examples
factor_levels <- c("one", "two", "three")
predicted_label <- factor(c("one", "two", "three"), levels = factor_levels)
observed <- factor(c("one", "three", "two"), levels = factor_levels)
predicted <- matrix(c(0.8, 0.1, 0.4,
0.1, 0.2, 0.4,
0.1, 0.7, 0.2),
nrow = 3)
logs_nominal(observed, predicted, predicted_label)
#> [1] 0.2231436 0.3566749 0.9162907