Fit a random forest with custom tuning
Now that you've explored the default tuning grids provided by the train() function, let's customize your models a bit more.
You can provide any number of values for mtry, from 2 up to the number of columns in the dataset. In practice, there are diminishing returns for much larger values of mtry, so you will use a custom tuning grid that explores 2 simple models (mtry = 2 and mtry = 3) as well as one more complicated model (mtry = 7).
This exercise is part of the course
Machine Learning with caret in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Define the tuning grid: tuneGrid
tuneGrid <- data.frame(
.mtry = ___,
.splitrule = "___",
.min.node.size = ___
)