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).