#' Computes classification accuracy from the confusion matrix summary based on a
#' set of predicted and truth classes for a signature.
#'
#' For each class, we calculate the classification accuracy in order to summarize
#' its performance for the signature. We compute one of two aggregate scores,
#' to summarize the overall performance of the signature.
#'
#' We define the accuracy as the proportion of correct classifications.
#'
#' The two aggregate score options are the macro- and micro-aggregate (average)
#' scores. The macro-aggregate score is the arithmetic mean of the binary scores
#' for each class. The micro-aggregate score is a weighted average of each class'
#' binary score, where the weights are determined by the sample sizes for each
#' class. By default, we use the micro-aggregate score because it is more robust,
#' but the macro-aggregate score might be more intuitive to some users.
#'
#' Note that the macro- and micro-aggregate scores are the same for classification
#' accuracy.
#'
#' The accuracy measure ranges from 0 to 1 with 1 being the optimal value.
#'
#' @export
#'
#' @rdname accuracy
#'
#' @param confusionSummary list containing the confusion summary for a set of
#' classifications
#'
#' @param aggregate string that indicates the type of aggregation; by default,
#' micro. See details.
#'
#' @return list with the accuracy measure for each class as well as the macro-
#' and micro-averages (aggregate measures across all classes).
#'
#' @examples
#'
#' data(prediction_values)
#'
#' # Create the confusion matrix
#' confmat <- confusion(prediction_values[,"Curated_Quality"], prediction_values[,"PredictClass"])
#'
#' accuracy(confmat)
#'
accuracy <- function(confusionSummary, aggregate = c('micro', 'macro')) {
aggregate <- match.arg(aggregate)
byClass <- sapply(confusionSummary$classSummary, function(clSummary) {
with(clSummary,
(truePos + trueNeg) / (truePos + trueNeg + falsePos + falseNeg)
)
})
names(byClass) <- names(confusionSummary$classSummary)
if (aggregate == 'micro') {
numerator <- sum(sapply(confusionSummary$classSummary, function(clSummary) {
with(clSummary, truePos + trueNeg)
}))
denom <- sum(sapply(confusionSummary$classSummary, function(clSummary) {
with(clSummary, truePos + trueNeg + falsePos + falseNeg)
}))
aggregate <- numerator/denom
} else {
aggregate <- mean(byClass)
}
list(byClass = byClass, aggregate = aggregate)
}
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