eCM_plot {explainer}

Enhanced Confusion Matrix Plot

Description

This function generates an enhanced confusion matrix plot using the CVMS package. The plot includes visualizations of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Usage

eCM_plot(task, trained_model, splits, add_sums = TRUE, palette = "Green")

Arguments

task

mlr3 task object specifying the task details

trained_model

mlr3 trained learner (model) object obtained after training

splits

mlr3 object defining data splits for train and test sets

add_sums

logical, indicating whether total numbers should be displayed in the plot (default: TRUE)

palette

character, the color palette for the confusion matrix (default: "Green")

Value

A confusion matrix plot visualizing sensitivity, specificity, PPV, and NPV

Examples

library("explainer")
seed <- 246
set.seed(seed)

# Load necessary packages
if (!requireNamespace("mlbench", quietly = TRUE)) stop("mlbench not installed.")
if (!requireNamespace("mlr3learners", quietly = TRUE)) stop("mlr3learners not installed.")
if (!requireNamespace("ranger", quietly = TRUE)) stop("ranger not installed.")
# Load BreastCancer dataset
utils::data("BreastCancer", package = "mlbench")
target_col <- "Class"
positive_class <- "malignant"
mydata <- BreastCancer[, -1]
mydata <- na.omit(mydata)
sex <- sample(
  c("Male", "Female"),
  size = nrow(mydata),
  replace = TRUE
)
mydata$age <- as.numeric(sample(
  seq(18,60),
  size = nrow(mydata),
  replace = TRUE
))
mydata$sex <- factor(
  sex,
  levels = c("Male", "Female"),
  labels = c(1, 0)
)
maintask <- mlr3::TaskClassif$new(
  id = "my_classification_task",
  backend = mydata,
  target = target_col,
  positive = positive_class
)
splits <- mlr3::partition(maintask)
mylrn <- mlr3::lrn(
  "classif.ranger",
  predict_type = "prob"
)
mylrn$train(maintask, splits$train)
myplot <- eCM_plot(
  task = maintask,
  trained_model = mylrn,
  splits = splits
)