details_decision_tree_rpart: Decision trees via CART

In parsnip: A Common API to Modeling and Analysis Functions

Decision trees via CART

Description

rpart::rpart() fits a model as a set of ⁠if/then⁠ statements that creates a tree-based structure.

Details

For this engine, there are multiple modes: classification, regression, and censored regression

Tuning Parameters

This model has 3 tuning parameters:

• tree_depth: Tree Depth (type: integer, default: 30L)

• min_n: Minimal Node Size (type: integer, default: 2L)

• cost_complexity: Cost-Complexity Parameter (type: double, default: 0.01)

Translation from parsnip to the original package (classification)

decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% 
  set_engine("rpart") %>% 
  set_mode("classification") %>% 
  translate()

## Decision Tree Model Specification (classification)
## 
## Main Arguments:
##   cost_complexity = double(1)
##   tree_depth = integer(1)
##   min_n = integer(1)
## 
## Computational engine: rpart 
## 
## Model fit template:
## rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
##     cp = double(1), maxdepth = integer(1), minsplit = min_rows(0L, 
##         data))

Translation from parsnip to the original package (regression)

decision_tree(tree_depth = integer(1), min_n = integer(1), cost_complexity = double(1)) %>% 
  set_engine("rpart") %>% 
  set_mode("regression") %>% 
  translate()

## Decision Tree Model Specification (regression)
## 
## Main Arguments:
##   cost_complexity = double(1)
##   tree_depth = integer(1)
##   min_n = integer(1)
## 
## Computational engine: rpart 
## 
## Model fit template:
## rpart::rpart(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
##     cp = double(1), maxdepth = integer(1), minsplit = min_rows(0L, 
##         data))

Translation from parsnip to the original package (censored regression)

The censored extension package is required to fit this model.

library(censored)

decision_tree(
  tree_depth = integer(1),
  min_n = integer(1),
  cost_complexity = double(1)
) %>% 
  set_engine("rpart") %>% 
  set_mode("censored regression") %>% 
  translate()

## Decision Tree Model Specification (censored regression)
## 
## Main Arguments:
##   cost_complexity = double(1)
##   tree_depth = integer(1)
##   min_n = integer(1)
## 
## Computational engine: rpart 
## 
## Model fit template:
## pec::pecRpart(formula = missing_arg(), data = missing_arg(), 
##     weights = missing_arg(), cp = double(1), maxdepth = integer(1), 
##     minsplit = min_rows(0L, data))

Preprocessing requirements

This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. ⁠{a, c}⁠ vs ⁠{b, d}⁠) when splitting at a node. Dummy variables are not required for this model.

Case weights

This model can utilize case weights during model fitting. To use them, see the documentation in case_weights and the examples on tidymodels.org.

The fit() and fit_xy() arguments have arguments called case_weights that expect vectors of case weights.

Other details

Predictions of type "time" are predictions of the mean survival time.

Saving fitted model objects

This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.

Examples

The “Fitting and Predicting with parsnip” article contains examples for decision_tree() with the "rpart" engine.

References

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.

parsnip documentation built on June 24, 2024, 5:14 p.m.