# createDataPartition

0th

Percentile

##### Data Splitting functions

A series of test/training partitions are created using createDataPartition while createResample creates one or more bootstrap samples. createFolds splits the data into k groups.

Keywords
utilities
##### Usage
createDataPartition(y, times = 1, p = 0.5, list = TRUE,
groups = min(5, length(y)))
createResample(y, times = 10, list = TRUE)
createFolds(y, k = 10, list = TRUE, returnTrain = FALSE)
##### Arguments
y
a vector of outcomes
times
the number of partitions to create
p
the percentage of data that goes to training
list
logical - should the results be in a list (TRUE) or a matrix with the number of rows equal to floor(p * length(y)) and times columns.
groups
for numeric y, the number of breaks in the quantiles (see below)
k
an integer for the number of folds.
returnTrain
a logical. When true, the values returned are the sample positions corresponding to the data used during training. This argument only works in conjunction with list = TRUE
##### Details

For bootstrap samples, simple random sampling is used.

For other data splitting, the random sampling is done within the levels of y when y is a factor in an attempt to balance the class distributions within the splits. For numeric y, the sample is split into groups sections based on quantiles and sampling is done within these subgroups. Also, for very small class sizes (

##### Value

• A list or matrix of row positions (e.g. 1, 15) corresponding to the training data

##### Aliases
• createDataPartition
• createResample
• createFolds
##### Examples
data(oil)
createDataPartition(oilType, 2)

x <- rgamma(50, 3, .5)
inA <- createDataPartition(x, list = FALSE)

plot(density(x[inA]))
rug(x[inA])

points(density(x[-inA]), type = "l", col = 4)
rug(x[-inA], col = 4)

createResample(oilType, 2)

createFolds(oilType, 10)
createFolds(oilType, 5, FALSE)

createFolds(rnorm(21))
Documentation reproduced from package caret, version 3.21, License: GPL-2

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