Nothing
#' @title get_CLE_comparison
#' @description visualises how class 1 and class 0 classification error (CLE) differs in each trained calibration model.
#' Comparing class-specific CLE helps to choose a calibration model for applications were classification error is cost-sensitive for one class.
#' See \code{\link{get_CLE_class}} for details on the implementation.
#' @param list_models list object that contains all error values for all trained calibration models. For the specific format, see the calling function \code{\link{visualize_calibratR}}.
#' @return ggplot2
#' @rdname get_CLE_comparison
get_CLE_comparison <- function(list_models){
list_models$original <- NULL
list_errors_0 <- list()
list_errors_1 <- list()
idx <- 1
for (j in list_models){
list_errors_1[[names(list_models)[[idx]]]] <- j$CLE_class_1
list_errors_0[[names(list_models)[[idx]]]] <- j$CLE_class_0
idx <- idx+1
}
df_cle_0 <- cbind(reshape2::melt(list_errors_0), Class="CLE class 0")
df_cle_1 <- cbind(reshape2::melt(list_errors_1), Class="CLE class 1")
df <- rbind(df_cle_0, df_cle_1)
Class <- NULL
value <- NULL
L1 <- NULL
ggplot2::ggplot(df, ggplot2::aes(x=L1, y=value, colour=Class)) +
ggplot2::ggtitle("Class-specific CLE") +
ggplot2::scale_x_discrete(name = NULL) +
ggplot2::geom_boxplot() +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 60, hjust = 1))
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.