randomUniformForest (version 1.1.2)

postProcessingVotes: Post-processing for Regression

Description

Post-processing use OOB votes and predicted values to build more accurate estimates of Response values. Note that post-processing can not ensure that new estimate will have a lower error. It works for many cases but not all.

Usage

postProcessingVotes(object,
	nbModels = 1,
	idx = 1,
	granularity = 1,
	predObject = NULL,
	swapPredictions = FALSE,
	X = NULL, 
	imbalanced = FALSE)

Value

• a vector of predicted values.

Examples

# Note that post-processing works better with enough trees, at least 100, and enough data
n = 200;  p = 20
# Simulate 'p' gaussian vectors with random parameters between -10 and 10.
X <- simulationData(n,p)

# give names to features
X <- fillVariablesNames(X)

# Make a rule to create response vector
epsilon1 = runif(n,-1,1)
epsilon2 = runif(n,-1,1)

# a rule with many noise (only four variables are significant)
Y = 2*(X[,1]*X[,2] + X[,3]*X[,4]) + epsilon1*X[,5] + epsilon2*X[,6]

# randomize then  make train and test sample
twoSamples <- cut(sample(1:n,n), 2, labels = FALSE)

Xtrain = X[which(twoSamples == 1),]
Ytrain = Y[which(twoSamples == 1)]
Xtest = X[which(twoSamples == 2),]
Ytest = Y[which(twoSamples == 2)]

# compute an accurate model (in this case bagging and log data works best) and predict
rUF.model <- randomUniformForest(Xtrain, Ytrain, xtest = Xtest, ytest = Ytest, 
bagging = TRUE, logX = TRUE, ntree = 60, threads = 2)

# get mean squared error
rUF.model

# post process
newEstimate <- postProcessingVotes(rUF.model)

# get mean squared error
sum((newEstimate - Ytest)^2)/length(Ytest)

## regression do not use all data but sub-samples.
## compare, when using full sample. But then, we do not have OOB data

# rUF.model.fullsample <- randomUniformForest(Xtrain, Ytrain, xtest = Xtest, ytest = Ytest,
# subsamplerate = 1, bagging = TRUE, logX = TRUE, ntree = 60, threads = 2)
# rUF.model.fullsample

## Nevertheless we can use old model with OOB data to fit a new estimate
## newEstimate.fullsample <- postProcessingVotes(rUF.model.fullsample,
# predObject = rUF.model, swapPredictions = TRUE)

## get mean squared error
# sum((newEstimate.fullsample - Ytest)^2)/length(Ytest)