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Last data update: 2014.03.03

R: Post-processing for Regression

postProcessingVotes

R Documentation

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)

Arguments

`object`	a randomUniformForest object with OOB data.
`nbModels`	how many models to build for new estimates. Usually one is enough.
`idx`	how many values to choose in OOB model for each new predicted value. Usually one is enough.
`granularity`	degree of precision needed for each old estimate value. Usually one is enough.
`predObject`	if current model is built with full sample, then using an old model 'predObject' (a randomUniformForest object) that have OOB data can help to reduce error. Must be used with 'swapPredictions = TRUE'
`swapPredictions`	set it to TRUE if two models, current one without OOB data and old one with OOB data, have to be used for trying to reduce prediction error.
`X`	not currently used.
`imbalanced`	not currently used.

Value

a vector of predicted values.

Author(s)

Saip Ciss saip.ciss@wanadoo.fr

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)