details_bag_tree_rpart: Bagged trees via rpart

In parsnip: A Common API to Modeling and Analysis Functions

Bagged trees via rpart

Description

baguette::bagger() and ipred::bagging() create collections of decision trees forming an ensemble. All trees in the ensemble are combined to produce a final prediction.

Details

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

Tuning Parameters

This model has 4 tuning parameters:

• class_cost: Class Cost (type: double, default: (see below))

• 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)

For the class_cost parameter, the value can be a non-negative scalar for a class cost (where a cost of 1 means no extra cost). This is useful for when the first level of the outcome factor is the minority class. If this is not the case, values between zero and one can be used to bias to the second level of the factor.

Translation from parsnip to the original package (classification)

The baguette extension package is required to fit this model.

library(baguette)

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

## Bagged 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:
## baguette::bagger(formula = missing_arg(), data = missing_arg(), 
##     weights = missing_arg(), cp = double(1), maxdepth = integer(1), 
##     minsplit = integer(1), base_model = "CART")

Translation from parsnip to the original package (regression)

The baguette extension package is required to fit this model.

library(baguette)

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

## Bagged 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:
## baguette::bagger(formula = missing_arg(), data = missing_arg(), 
##     weights = missing_arg(), cp = double(1), maxdepth = integer(1), 
##     minsplit = integer(1), base_model = "CART")

Translation from parsnip to the original package (censored regression)

The censored extension package is required to fit this model.

library(censored)

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

## Bagged 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:
## ipred::bagging(formula = missing_arg(), data = missing_arg(), 
##     weights = missing_arg(), cp = double(1), maxdepth = integer(1), 
##     minsplit = integer(1))

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 median survival time.

References

• Breiman L. 1996. “Bagging predictors”. Machine Learning. 24 (2): 123-140

• Hothorn T, Lausen B, Benner A, Radespiel-Troeger M. 2004. Bagging Survival Trees. Statistics in Medicine, 23(1), 77–91.

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

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