cla_rf: Random Forest for classification

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Random Forest for classification

Description

Ensemble classifier of decision trees using randomForest::randomForest.

Usage

cla_rf(attribute, slevels, nodesize = 5, ntree = 10, mtry = NULL)

Arguments

attribute	attribute target to model building
slevels	possible values for the target classification
nodesize	node size
ntree	number of trees
mtry	number of attributes to build tree

Details

Combines many decorrelated trees to reduce variance. Key hyperparameters: ntree, mtry, nodesize.

Value

returns a classification object

References

Breiman, L. (2001). Random Forests. Machine Learning 45(1):5–32. Liaw, A. and Wiener, M. (2002). Classification and Regression by randomForest. R News.

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_rf("Species", slevels, ntree=5)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, iris)
train <- sr$train
test <- sr$test

model <- fit(model, train)

prediction <- predict(model, test)
predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics

daltoolbox documentation built on Nov. 5, 2025, 7:09 p.m.