train
functiontrainControl(method = "boot",
number = ifelse(grepl("cv", method), 10, 25),
repeats = ifelse(grepl("cv", method), 1, number),
p = 0.75,
search = "grid",
initialWindow = NULL,
horizon = 1,
fixedWindow = TRUE,
verboseIter = FALSE,
returnData = TRUE,
returnResamp = "final",
savePredictions = FALSE,
classProbs = FALSE,
summaryFunction = defaultSummary,
selectionFunction = "best",
preProcOptions = list(thresh = 0.95, ICAcomp = 3, k = 5),
sampling = NULL,
index = NULL,
indexOut = NULL,
timingSamps = 0,
predictionBounds = rep(FALSE, 2),
seeds = NA,
adaptive = list(min = 5, alpha = 0.05,
method = "gls", complete = TRUE),
trim = FALSE,
allowParallel = TRUE)
"boot"
, "boot632"
, "cv"
, "repeatedcv"
,
"LOOCV"
, "LGOCV"
(for repeated training/test splits), "none"
(only fits one model to the entire t"final"
, "all"
or "none"
"all"
, "final"
, or "none"
. A logical value can also be used that convert to "all"
(for true) o"grid"
or "random"
, describing how the tuning parameter grid is determined. See details below.createTimeSlices
defaultSummary
.best
for details and other options.preProcess
. The type of pre-processing (e.g. center, scaling etc) is passed in via the preProc
option in train
."none"
, "down"
, "up"
, "smote"
, or "rose"
index
) that dictates which data are held-out for each resample (as integers). If NULL
, then the unique set of samples not contained in index
is used.c(TRUE, FALSE)
would only constrain the lower end of predicNA
will stop the seed from being set within the worker processes while a value of method
is "adaptive_cv"
, "adaptive_boot"
or "adaptive_LGOCV"
. See Details below.TRUE
the final model in object$finalModel
may have some components of the object removed so reduce the size of the saved object. The predict
method will still work, but some other features of the model train
does some optimizations for certain models. For example, when tuning over PLS model, the only model that is fit is the one with the largest number of components. So if the model is being tuned over comp in 1:10
, the only model fit is ncomp = 10
. However, if the vector of integers used in the seeds
arguments is longer than actually needed, no error is thrown.Using method = "none"
and specifying more than one model in train
's tuneGrid
or tuneLength
arguments will result in an error.
Using adaptive resampling when method
is either "adaptive_cv"
, "adaptive_boot"
or "adaptive_LGOCV"
, the full set of resamples is not run for each model. As resampling continues, a futility analysis is conducted and models with a low probability of being optimal are removed. These features are experimental. See Kuhn (2014) for more details. The options for this procedure are:
min
: the minimum number of resamples used before models are removedalpha
: the confidence level of the one-sided intervals used to measure futilitymethod
: either generalized least squares (method = "gls"
) or a Bradley-Terry model (method = "BT"
)complete
: if a single parameter value is found before the end of resampling, should the full set of resamples be computed for that parameter. )The option search = "grid"
uses the default grid search routine. When search = "random"
, a random search procedure is used (Bergstra and Bengio, 2012). See
Kuhn (2014), ``Futility Analysis in the Cross-Validation of Machine Learning Models''
Package website for subsampling:
## Do 5 repeats of 10-Fold CV for the iris data. We will fit
## a KNN model that evaluates 12 values of k and set the seed
## at each iteration.
set.seed(123)
seeds <- vector(mode = "list", length = 51)
for(i in 1:50) seeds[[i]] <- sample.int(1000, 22)
## For the last model:
seeds[[51]] <- sample.int(1000, 1)
ctrl <- trainControl(method = "repeatedcv",
repeats = 5,
seeds = seeds)
set.seed(1)
mod <- train(Species ~ ., data = iris,
method = "knn",
tuneLength = 12,
trControl = ctrl)
ctrl2 <- trainControl(method = "adaptive_cv",
repeats = 5,
verboseIter = TRUE,
seeds = seeds)
set.seed(1)
mod2 <- train(Species ~ ., data = iris,
method = "knn",
tuneLength = 12,
trControl = ctrl2)
Run the code above in your browser using DataCamp Workspace