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).

Deze oefening maakt deel uit van de cursus

Machine Learning with caret in R

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Define the tuning grid: tuneGrid
tuneGrid <- data.frame(
  .mtry = ___,
  .splitrule = "___",
  .min.node.size = ___
)

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