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

obj

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 May 29, 2024, 1:57 a.m.