Takes in data, fraction (for train set) and seed, and returns train and test set
a matrix, data.frame or data.table
proportion of observations that should go in the train set
an integer value
An essential task before doing modeling is to split the modeling data into
train and test sets.
splitdata is built for this task and returns a list
with train and test sets, which can be picked using the code given in example.
fraction corresponds to the train dataset, while the rest of the
observations go to the test dataset. If the user wants to generate the same
test and train dataset everytime, he should specify a
a list with two elements: train and test set
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# A 'data.frame' df <- data.frame(x = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), y = c('a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'), z = c(1, 1, 0, 0, 1, 0, 0, 1, 1, 0)) # Split data into train (70%) and test (30%) ltData <- splitdata(data = df, fraction = 0.7, seed = 123) trainData <- ltData$train testData <- ltData$test