daltoolbox (version 1.2.747)

cla_rf: Random Forest for classification

Description

Ensemble classifier of decision trees using randomForest::randomForest.

Usage

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

Value

returns a classification object

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.

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