Decision trees via rpart — rpart

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.

Usage

rpart_train(
  formula,
  data,
  weights = NULL,
  cp = 0.01,
  minsplit = 20,
  maxdepth = 30,
  ...
)

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.

A fitted rpart model.