splitquoted: splitQuoted

In Mthrun/Classifiers: Common Supervised Machine Learning Algorithms

splitQuoted

Description

Quota-split of Data randomly into disjunct sets TrainCls and TestCls, with |TrainCls| = |TestCls|*Percentage/100 splitting is done such that the percentages are the same over all classes. Data can be also divided into disjunct set in this function otherwise use indices to split Data in a later step

Usage

splitQuoted(Cls,Data,Percentage, Key,LowLim)

Arguments

Cls	[1:n] numeric vector of classifications
Data	Optional, [1:n,1:d] Matrix of n cases and d features, or n d-dimensional datapoints
Percentage	Scalar, value between 1 and 99, default is 80
Key	Optional, [1:n] vector, containing the keys. Default is either 1:length(Cls) or rownames(Data) if existing
LowLim	Optional, Scalar giving the minimum number if cases per class, if the minimum is higher than the number of elements in class, sampling is done with replacement

Details

Quota-split of Data randomly into disjunct sets TrainData and TestData, with |TrainData| = |Data|*Percentage/100. Splitting is done such that the percentages are the same over all classes.

Value

List with

TrainInd	Numeric vector, containing the indices of the the Cls/Data entries in the Train split
TestInd	Numeric vector, containing the indices of the the Cls entries in the Test split'
TrainCls	Numeric vector, containing the cls entries in the Train split
TestCls	Numeric vector, containing the cls entries in the Test split
TrainData	Numeric vector, containing the Data entries in the Train split. If Data is missing, then TrainData is NULL
TestData	Numeric vector, containing the Data entries in the Test split. If Data is missing, then TestData is NULL
TrainKey	Numeric vector, containing the key entries in the Train split
TestKey	Numeric vector, containing the key entries in the Test split

Author(s)

Michael Thrun

Examples

data("USelectionPoll")
V=splitEqual(USelectionPoll$Cls,Percentage=50)

Mthrun/Classifiers documentation built on June 28, 2023, 9:28 a.m.