## Not run:
#
# #######################################
# ## Classification Example
#
# data(iris)
# TrainData <- iris[,1:4]
# TrainClasses <- iris[,5]
#
# knnFit1 <- train(TrainData, TrainClasses,
# method = "knn",
# preProcess = c("center", "scale"),
# tuneLength = 10,
# trControl = trainControl(method = "cv"))
#
# knnFit2 <- train(TrainData, TrainClasses,
# method = "knn",
# preProcess = c("center", "scale"),
# tuneLength = 10,
# trControl = trainControl(method = "boot"))
#
#
# library(MASS)
# nnetFit <- train(TrainData, TrainClasses,
# method = "nnet",
# preProcess = "range",
# tuneLength = 2,
# trace = FALSE,
# maxit = 100)
#
# #######################################
# ## Regression Example
#
# library(mlbench)
# data(BostonHousing)
#
# lmFit <- train(medv ~ . + rm:lstat,
# data = BostonHousing,
# method = "lm")
#
# library(rpart)
# rpartFit <- train(medv ~ .,
# data = BostonHousing,
# method = "rpart",
# tuneLength = 9)
#
# #######################################
# ## Example with a custom metric
#
# madSummary <- function (data,
# lev = NULL,
# model = NULL) {
# out <- mad(data$obs - data$pred,
# na.rm = TRUE)
# names(out) <- "MAD"
# out
# }
#
# robustControl <- trainControl(summaryFunction = madSummary)
# marsGrid <- expand.grid(degree = 1, nprune = (1:10) * 2)
#
# earthFit <- train(medv ~ .,
# data = BostonHousing,
# method = "earth",
# tuneGrid = marsGrid,
# metric = "MAD",
# maximize = FALSE,
# trControl = robustControl)
#
# #######################################
# ## Parallel Processing Example via multicore package
#
# ## library(doMC)
# ## registerDoMC(2)
#
# ## NOTE: don't run models form RWeka when using
# ### multicore. The session will crash.
#
# ## The code for train() does not change:
# set.seed(1)
# usingMC <- train(medv ~ .,
# data = BostonHousing,
# method = "glmboost")
#
# ## or use:
# ## library(doMPI) or
# ## library(doParallel) or
# ## library(doSMP) and so on
#
# ## End(Not run)
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