Chapter 47 Modeling with R caret

library(caret)
library('ISLR')
df <- Default
head(df)

# Check for parameters of model to test
modelLookup('c5.0')

# Test parameters of models
set.seed(123)
m <- caret::train(default ~ ., data = df, method = "C5.0")

p <- predict(m, df)

table(p, df$default)

head(predict(m, df, type = "prob"))

# Resampling methods
?trainControl()

## TUNING PARAMETERS OF THE MODEL
# example
ctrl <- trainControl(method = "cv", number = 10, selectionFunction = "oneSE")

# grid to test several parameters
grid <- expand.grid(.model = "tree",
                    .trials = c(1, 5, 10, 15, 20, 25, 30, 35),
                    .winnow = "FALSE")

set.seed(300)
m <- train(default ~ ., data = credit, method = "C5.0",
           metric = "Kappa",
           trControl = ctrl,
           tuneGrid = grid)

PCA
The predictors should be centered and scaled before applying this transformation.

# See available algorithms in caret
modelnames <- paste(names(getModelInfo()), collapse=',  ')
modelnames

modelLookup(algo)