details_bag_tree_C5.0: Bagged trees via C5.0

In parsnip: A Common API to Modeling and Analysis Functions

Bagged trees via C5.0

Description

baguette::bagger() creates an collection of decision trees forming an ensemble. All trees in the ensemble are combined to produce a final prediction.

Details

For this engine, there is a single mode: classification

Tuning Parameters

This model has 1 tuning parameters:

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

Translation from parsnip to the original package (classification)

The baguette extension package is required to fit this model.

library(baguette)

bag_tree(min_n = integer()) %>% 
  set_engine("C5.0") %>% 
  set_mode("classification") %>% 
  translate()

## Bagged Decision Tree Model Specification (classification)
## 
## Main Arguments:
##   cost_complexity = 0
##   min_n = integer()
## 
## Computational engine: C5.0 
## 
## Model fit template:
## baguette::bagger(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
##     minCases = integer(), base_model = "C5.0")

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.

References

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

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

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