pruneTree: Merge stumps with repeated conditions

In nivangio/adaStump: Fitting adaBoost model with Stumps as base learners

Description

In order to reduce execution times, stumps with the same condition are collapsed. If a pruned model is used, n.iter can no longer be specified and all the stumps are used.

Usage

pruneTree(stump.model)

Arguments

stump.model

Object of class "adaStump" to be reduced.

Value

An item of class prunedadaStump containing the following:

model	a data.frame describing the pruned stumps
type	Type of ada execution performed.

Examples

#Load Iris
data(iris)

#Create Variable is Iris as numerical
iris$isSetosa <- as.numeric(iris$Species == "setosa")

#Split sample in 70 train - 30 test
train.ind <- sample(nrow(iris), nrow(iris) * 0.7)

#Train model. For obvious reasons, Species variable is not included in the fit
fit <- adaStump(isSetosa ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, iris[train.ind,], 
                type = "discrete", iter = 10, nu = 0.05, bag.frac = 0.6)
                
#Prune Tree and predict

fit_pruned <- pruneTree(fit)
predict(fit_pruned,iris[-train.ind,])

nivangio/adaStump documentation built on May 23, 2019, 7:06 p.m.