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.