Split Data into Test and Train Set
Split data from vector Y into two sets in predefined ratio while preserving relative ratios of different labels in Y. Used to split the data used during classification into train and test subsets.
sample.split( Y, SplitRatio = 2/3, group = NULL )
Y |
Vector of data labels. If there are only a few labels (as is expected) than relative ratio of data in both subsets will be the same. |
SplitRatio |
Splitting ratio:
|
group |
Optional vector/list used when multiple copies of each sample
are present. In such a case |
Function msc.sample.split is the old name of the
sample.split function. To be retired soon. Note that the function
differs from base::sample by first restricting the input data set
to its unique values before generating the subset(s).
Returns logical vector of the same length as Y with random
SplitRatio*length(Y) elements set to TRUE.
Jarek Tuszynski (SAIC) jaroslaw.w.tuszynski@saic.com
library(MASS)
data(cats) # load cats data
Y = cats[,1] # extract labels from the data
msk = sample.split(Y, SplitRatio=3/4)
table(Y,msk)
t=sum( msk) # number of elements in one class
f=sum(!msk) # number of elements in the other class
stopifnot( round((t+f)*3/4) == t ) # test ratios
# example of using group variable
g = rep(seq(length(Y)/4), each=4); g[48]=12;
msk = sample.split(Y, SplitRatio=1/2, group=g)
table(Y,msk) # try to get correct split ratios ...
split(msk,g) # ... while keeping samples with the same group label together
# test results
print(paste( "All Labels numbers: total=",t+f,", train=",t,", test=",f,
", ratio=", t/(t+f) ) )
U = unique(Y) # extract all unique labels
for( i in 1:length(U)) { # check for all labels
lab = (Y==U[i]) # mask elements that have label U[i]
t=sum( msk[lab]) # number of elements with label U[i] in one class
f=sum(!msk[lab]) # number of elements with label U[i] in the other class
print(paste( "Label",U[i],"numbers: total=",t+f,", train=",t,", test=",f,
", ratio=", t/(t+f) ) )
}
# use results
train = cats[ msk,2:3] # use output of sample.split to ...
test = cats[!msk,2:3] # create train and test subsets
z = lda(train, Y[msk]) # perform classification
table(predict(z, test)$class, Y[!msk]) # predicted & true labels
# see also LogitBoost examplePlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.