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#######################################
## 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)
#######################################
## Example with a recipe
data(cox2)
cox2 <- cox2Descr
cox2$potency <- cox2IC50
library(recipes)
cox2_recipe <- recipe(potency ~ ., data = cox2) %>%
## Log the outcome
step_log(potency, base = 10) %>%
## Remove sparse and unbalanced predictors
step_nzv(all_predictors()) %>%
## Surface area predictors are highly correlated so
## conduct PCA just on these.
step_pca(contains("VSA"), prefix = "surf_area_",
threshold = .95) %>%
## Remove other highly correlated predictors
step_corr(all_predictors(), -starts_with("surf_area_"),
threshold = .90) %>%
## Center and scale all of the non-PCA predictors
step_center(all_predictors(), -starts_with("surf_area_")) %>%
step_scale(all_predictors(), -starts_with("surf_area_"))
set.seed(888)
cox2_lm <- train(cox2_recipe,
data = cox2,
method = "lm",
trControl = trainControl(method = "cv"))
#######################################
## 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
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