tidy conditional inference tree. Creates easily interpretable decision tree models that be shown with the visualize_model function.
Statistical significance required for a split , and minimum necessary samples in a terminal leaf can be controlled to create the desired tree visual.
tidy_ctree(.data, formula, minbucket = 7L, mincriterion = 0.95, ...)Arguments
- .data
dataframe
- formula
formula
- minbucket
minimum amount of samples in terminal leaves, default is 7
- mincriterion
(1 - alpha) value between 0 -1, default is .95. lowering this value creates more splits, but less significant
- ...
optional parameters to
ctree_control
Value
a ctree object
Examples
iris %>%
tidy_formula(., Sepal.Length) -> sepal_form
iris %>%
tidy_ctree(sepal_form) %>%
visualize_model()
iris %>%
tidy_ctree(sepal_form, minbucket = 30) %>%
visualize_model(plot_type = "box")
