Decision trees via party — details_decision_tree

party::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 is a single mode: censored regression

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 (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("party") %>% 
  set_mode("censored regression") %>% 
  translate()

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

censored::cond_inference_surv_ctree() is a wrapper around party::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.

References

Hothorn T, Hornik K, Zeileis A. 2006. Unbiased Recursive Partitioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics, 15(3), 651–674.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.