createResamplecreates one or more bootstrap samples.
createFoldssplits the data into
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) createMultiFolds(y, k = 10, times = 5)
TRUE) or a matrix with the number of rows equal to
floor(p * length(y))and
y, the number of breaks in the quantiles (see below)
list = TRUE
For other data splitting, the random sampling is done within the
y is a factor in an attempt to balance
the class distributions within the splits. For numeric
sample is split into
groups sections based
on quantiles and sampling is done within these subgroups. Also, for
very small class sizes (<= 3)="" the="" classes="" may="" not="" show="" up="" in="" both="" training="" and="" test="" data<="" p="">
For multiple k-fold cross-validation, completely independent folds are created.
The names of the list objects will denote the fold membership using the pattern
"Foldi.Repj" meaning the ith section (of k) of the jth cross-validation set
times). Note that this function calls
list = TRUE and
returnTrain = TRUE.
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))