roc: Roc curve

In e2tree: Explainable Ensemble Trees

Roc curve

Description

Computes and plots the Receiver Operating Characteristic (ROC) curve for a binary classification model, along with the Area Under the Curve (AUC). The ROC curve is a graphical representation of a classifier’s performance across all classification thresholds.

Usage

roc(response, scores, target = "1")

Arguments

response	is the response variable vector
scores	is the probability vector of the prediction
target	is the target response class

Value

an object.

Examples

## Classification:
data(iris)

# Create training and validation set:
smp_size <- floor(0.75 * nrow(iris))
train_ind <- sample(seq_len(nrow(iris)), size = smp_size)
training <- iris[train_ind, ]
validation <- iris[-train_ind, ]
response_training <- training[,5]
response_validation <- validation[,5]

# Perform training:
ensemble <- randomForest::randomForest(Species ~ ., data=training, 
importance=TRUE, proximity=TRUE)

D <- createDisMatrix(ensemble, data=training, label = "Species", 
                            parallel = list(active=FALSE, no_cores = 1))

setting=list(impTotal=0.1, maxDec=0.01, n=2, level=5)
tree <- e2tree(Species ~ ., training, D, ensemble, setting)

pr <- ePredTree(tree, validation, target="setosa")

roc(response_training, scores = pr$score, target = "setosa")
 

e2tree documentation built on April 12, 2025, 9:11 a.m.