cla_rf: Random Forest for classification

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Random Forest for classification

Description

Creates a classification object that uses the Random Forest method It wraps the randomForest library.

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

Value

returns a classification object

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. 3, 2024, 9:06 a.m.