`rpart_train()`

is a wrapper for `rpart()`

tree-based models
where all of the model arguments are in the main function.

The function is now deprecated, as parsnip uses `rpart::rpart()`

directly.

## Arguments

- formula
A model formula.

- data
A data frame.

- weights
Optional case weights.

- cp
A non-negative number for complexity parameter. Any split that does not decrease the overall lack of fit by a factor of

`cp`

is not attempted. For instance, with anova splitting, this means that the overall R-squared must increase by`cp`

at each step. The main role of this parameter is to save computing time by pruning off splits that are obviously not worthwhile. Essentially, the user informs the program that any split which does not improve the fit by`cp`

will likely be pruned off by cross-validation, and that hence the program need not pursue it.- minsplit
An integer for the minimum number of observations that must exist in a node in order for a split to be attempted.

- maxdepth
An integer for the maximum depth of any node of the final tree, with the root node counted as depth 0. Values greater than 30

`rpart`

will give nonsense results on 32-bit machines. This function will truncate`maxdepth`

to 30 in those cases.- ...
Other arguments to pass to either

`rpart`

or`rpart.control`

.