postProcessingVotes {randomUniformForest}

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,
	Xtest = NULL,
	imbalanced = FALSE,
	OOB = FALSE,
	method = c("cutoff", "bias", "residuals"),
	keep2ndModel = FALSE, 
	largeData = 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.

Xtest

test data in the case of regression, for a more friendly output of the model.

imbalanced

if TRUE, may improve metrics in the case of imbalanced datasets.

OOB

if FALSE, does not use OOB informations.

method

for classification, if expected bias is enough high, one may use it as a method to improve AUC. Otherwise, use the default one, 'cutoff'. Both tend to get the same results, despite a few tests for now, but 'bias' method seems more robust. 'residuals' is used in regression only as a powerful but computationally intensive method and replaces the default internal one.

keep2ndModel

if TRUE, and for regression, keep the model based on residuals for further modelling and predictions.

largeData

if TRUE, and for regression, use rUniformForest.big to compute model for the residuals.

...

arguments to use for the computation of the model from the residuals.

Value

a vector of predicted values.

Author(s)

Saip Ciss [e-mail]

References

Xu, Ruo, Improvements to random forest methodology (2013). Graduate Theses and Dissertations. Paper 13052.

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.
## Comparing, 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)