details_decision_tree_partykit: Decision trees via partykit

In tidymodels/parsnip: A Common API to Modeling and Analysis Functions

Decision trees via partykit

Description

partykit::ctree() fits a model as a set of if/then statements that creates a tree-based structure using hypothesis testing methods.

Details

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

Tuning Parameters

This model has 2 tuning parameters:

• tree_depth: Tree Depth (type: integer, default: see below)

• min_n: Minimal Node Size (type: integer, default: 20L)

The tree_depth parameter defaults to 0 which means no restrictions are applied to tree depth.

An engine-specific parameter for this model is:

• mtry: the number of predictors, selected at random, that are evaluated for splitting. The default is to use all predictors.

Translation from parsnip to the original package (regression)

The bonsai extension package is required to fit this model.

library(bonsai)

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

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

Translation from parsnip to the original package (classification)

The bonsai extension package is required to fit this model.

library(bonsai)

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

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

parsnip::ctree_train() is a wrapper around partykit::ctree() (and other functions) that makes it easier to run this model.

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)) %>% 
  set_engine("partykit") %>% 
  set_mode("censored regression") %>% 
  translate()

## Decision Tree Model Specification (censored regression)
## 
## Main Arguments:
##   tree_depth = integer(1)
##   min_n = integer(1)
## 
## Computational engine: partykit 
## 
## Model fit template:
## parsnip::ctree_train(formula = missing_arg(), data = missing_arg(), 
##     weights = missing_arg(), maxdepth = integer(1), minsplit = min_rows(0L, 
##         data))

censored::cond_inference_surv_ctree() is a wrapper around partykit::ctree() (and other functions) that makes it easier to run this model.

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.

Other details

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

References

• partykit: A Modular Toolkit for Recursive Partytioning in R

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

tidymodels/parsnip documentation built on April 12, 2024, 2:14 a.m.