pre (version 0.7.2)

caret_pre_model: Model set up for train function of package caret

Description

caret_pre_model provides a model setup for the train function of package caret

Usage

caret_pre_model

Format

An object of class list of length 17.

Details

When tuning parameters of pre() with caret's train() function, always use the default S3 method (i.e., specify predictors and response variables through arguments x and y. When train.formula(), is used (i.e., when formula and data arguments are specified), train will internally call model.matrix() on data, which will code all categorical (factor) predictor variables as dummy variables, and will yield a different result than inputting the original factors, for most tree-based methods.

Examples

# NOT RUN {
 
library("caret")

## Prepare data:
airq <- airquality[complete.cases(airquality),]
y <- airq$Ozone
x <- airq[,-1]

## Apply caret with only pre's default settings (trControl and ntrees argument
## are employed here only to reduce computation time):

set.seed(42)
prefit1 <- train(x = x, y = y, method = caret_pre_model,
                 trControl = trainControl(number = 1),
                 ntrees = 25L)
prefit1

## Create custom tuneGrid:
set.seed(42)
tuneGrid <- caret_pre_model$grid(x = x, y = y,
                                 maxdepth = 3L:5L,
                                 learnrate = c(.01, .1),
                                 penalty.par.val = c("lambda.1se", "lambda.min"))
tuneGrid
## Apply caret (again, ntrees and trControl set only to reduce computation time):
prefit2 <- train(x = x, y = y, method = caret_pre_model,
                 trControl = trainControl(number = 1),
                 tuneGrid = tuneGrid, ntrees = 25L)
prefit2

## Get best tuning parameter values:
prefit2$bestTune
## Get predictions from model with best tuning parameters:
predict(prefit2, newdata = x[1:10, ])
plot(prefit2)

## Obtain tuning grid through random search over the tuning parameter space:
set.seed(42)
tuneGrid2 <- caret_pre_model$grid(x = x, y = y, search = "random", len = 10)
tuneGrid2
set.seed(42)
prefit3 <- train(x = x, y = y, method = caret_pre_model,
                 trControl = trainControl(number = 1, verboseIter = TRUE),
                 tuneGrid = tuneGrid2, ntrees = 25L)
prefit3

## Count response:
set.seed(42)
prefit4 <- train(x = x, y = y, method = caret_pre_model,
                 trControl = trainControl(number = 1),
                 ntrees = 25L, family = "poisson")
prefit4

## Binary factor response:
y_bin <- factor(airq$Ozone > mean(airq$Ozone))
set.seed(42)
prefit5 <- train(x = x, y = y_bin, method = caret_pre_model,
                 trControl = trainControl(number = 1),
                 ntrees = 25L, family = "binomial")
prefit5

## Factor response with > 2 levels:
x_multin <- airq[,-5]
y_multin <- factor(airq$Month)
set.seed(42)
prefit6 <- train(x = x_multin, y = y_multin, method = caret_pre_model,
                 trControl = trainControl(number = 1),
                 ntrees = 25L, family = "multinomial")
prefit6
# }