pre (version 1.0.1)

caret_pre_model: Model set up for train function of package caret

Description

caret_pre_model provides a model setup for function train of package caret. It allows for tuning arguments sampfrac, maxdepth, learnrate, mtry, use.grad and penalty.par.val.

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., if 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, which will yield a different result than inputting the original factors, for most tree-based methods.

caret_pre__model$parameters provides an overview of the parameters that can be tuned for function pre using caret. caret_pre_model$grid provides a function for creating a tuning grid (see Examples below).

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
# }