init_values: Training and validation samples from data

In randomUniformForest: Random Uniform Forests for Classification, Regression and Unsupervised Learning

Training and validation samples from data

Description

Draw training and test samples from data. Samples can be accessed by subsctioting original data or by their own references.

Usage

init_values(X, Y = NULL, sample.size = 0.5, 
data.splitting = "ALL", 
unit.scaling = FALSE, 
scaling = FALSE, 
regression = FALSE)

Arguments

X	a matrix or dataframe to be splitted in training and validation sample
Y	a response vector for the observed data.
sample.size	size of the needed training sample in proportion of the nulber of observations in original data.
data.splitting	not currently used.
unit.scaling	if TRUE, scale all data in X between 0 and 1, if they are all positive, or between -1 and 1.
scaling	if TRUE, centers and scales data, so each variable willhave mean 0 abd variance 1.
regression	if TRUE and scaling = TRUE, Y will also be scaled.

Value

a list with the following components :

xtrain	a matrix or data frame representing the training sample.
ytrain	a response vector representing the training responses according to the training sample.
xtest	a matrix or data frame representing the validation sample.
ytest	a response vector representing the validation responses according to the validation sample.
train_idx	subscripts of the training sample.
test_idx	subscripts of the validation sample.

Author(s)

Saip Ciss [e-mail]

Examples

data(iris)
Y <- iris$Species
X <- iris[,-which(colnames(iris) == "Species")]
trainingAndValidationsamples <- init_values(X, Y, sample.size = 0.5)

Xtrain = trainingAndValidationsamples$xtrain
Ytrain = trainingAndValidationsamples$ytrain
Xvalid = trainingAndValidationsamples$xtest
Yvalid = trainingAndValidationsamples$ytest

randomUniformForest documentation built on June 22, 2022, 1:05 a.m.