randomUniformForest (version 1.1.2)

rm.trees: Remove trees from a random Uniform Forest

Description

Remove any number of trees from a random Uniform Forest, especially in case of incremental learning.

Usage

rm.trees(rufObject, X = NULL, Y = NULL, 
	method = c("default", "random", "oldest", "newest", "optimal", "quantile"), 
	howMany = NULL, 
	rm.sample = 0.1)

Examples

## Classification : "breast cancer" data 
## (http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic))

## data(breastCancer)
# breastCancer.data <- breastCancer 

## remove ID (first column) and divide data in train and test set
# breastCancer.data = breastCancer.data[,-1]

# n <- nrow(breastCancer.data)
# p <- ncol(breastCancer.data)

# trainTestIdx <- cut(sample(1:n, n), 2, labels= FALSE)

## train examples
# breastCancer.data.train <- breastCancer.data[trainTestIdx == 1, -p]
# breastCancer.class.train <- as.factor(breastCancer.data[trainTestIdx == 1, p])

## rename class in benign (class 2) and malignant (class 4) to have a better view
# levels(breastCancer.class.train) = c("benign", "malignant")

## test data
# breastCancer.data.test <- breastCancer.data[trainTestIdx == 2, -p]
# breastCancer.class.test <- as.factor(breastCancer.data[trainTestIdx == 2, p])
# levels(breastCancer.class.test) = c("benign", "malignant")

## compute many trees in a fast way, removing OOB evaluation
## breastCancer.ruf <- randomUniformForest(breastCancer.data.train, breastCancer.class.train, 
## classwt = c(1, 3.5), maxnodes = 10, OOB = FALSE, ntree = 500, threads = 2)

## predict and see results
## pred.breastCancer.ruf <- predict(breastCancer.ruf, breastCancer.data.test)
# confusion.matrix(as.numeric(pred.breastCancer.ruf), as.numeric(breastCancer.class.test))

## remove some trees at random. Default method can not be used since we don't have OOB data
# breastCancer.ruf <- rm.trees(breastCancer.ruf, method = "random", rm.sample = 0.2)

# breastCancer.ruf # old number of trees is still printed, but trees have been removed

## predict and see results again 
# pred.breastCancer.ruf <- predict(breastCancer.ruf, breastCancer.data.test)
# confusion.matrix(as.numeric(pred.breastCancer.ruf), as.numeric(breastCancer.class.test))