#896 Update documentation of log score (#918)

* use neg_log_score

* update docs, revert previous change

* update docs

R/metrics-binary.R

@@ -80,6 +80,7 @@ brier_score <- function(observed, predicted) {
 #' @importFrom methods hasArg
 #' @export
 #' @keywords metric
+#' @family log score functions
 #' @rdname scoring-functions-binary
 logs_binary <- function(observed, predicted) {
   assert_input_binary(observed, predicted)

R/metrics-nominal.R

@@ -18,6 +18,7 @@
 #' @export
 #' @keywords metric
 #' @rdname scoring-functions-nominal
+#' @family log score functions
 #' @examples
 #' factor_levels <- c("one", "two", "three")
 #' predicted_label <- factor(c("one", "two", "three"), levels = factor_levels)

R/metrics-sample.R

@@ -149,19 +149,23 @@ se_mean_sample <- function(observed, predicted) {
 #' [`logs_sample()`][scoringRules::scores_sample_univ] function from the
 #' \pkg{scoringRules} package.
 #'
+#' The log score is the negative logarithm of the predictive density evaluated
+#' at the observed value.
+#'
 #' The function should be used to score continuous predictions only.
 #' While the Log Score is in theory also applicable
-#' to discrete forecasts, the problem lies in the implementation: The Log score
-#' needs a kernel density estimation, which is not well defined with
-#' integer-valued Monte Carlo Samples. The Log score can be used for specific
-#' discrete probability distributions. See the scoringRules package for
-#' more details.
+#' to discrete forecasts, the problem lies in the implementation: The function
+#' uses a kernel density estimation, which is not well defined with
+#' integer-valued Monte Carlo Samples.
+#' See the scoringRules package for more details and alternatives, e.g.
+#' calculating scores for specific discrete probability distributions.
 #' @inheritParams ae_median_sample
 #' @param ... Additional arguments passed to
 #' [logs_sample()][scoringRules::logs_sample()] from the scoringRules package.
 #' @inheritSection illustration-input-metric-sample Input format
 #' @return Vector with scores.
 #' @importFrom scoringRules logs_sample
+#' @family log score functions
 #' @examples
 #' observed <- rpois(30, lambda = 1:30)
 #' predicted <- replicate(200, rpois(n = 30, lambda = 1:30))