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Poisson Decision Trees with Exposures

Source: R/decision_tree_exposure.R
decision_tree_exposure.Rd

decision_tree_exposure() defines a Poisson decision tree model with weighted exposures (observation times).

Usage

decision_tree_exposure(
  mode = "regression",
  engine = "rpart_exposure",
  cost_complexity = NULL,
  tree_depth = NULL,
  min_n = NULL
)

Arguments

mode

A single character string for the type of model. The only possible value for this model is "regression"

engine

A single character string specifying what computational engine to use for fitting.

cost_complexity

A positive number for the the cost/complexity parameter (a.k.a. Cp) used by CART models (specific engines only).

tree_depth

An integer for maximum depth of the tree.

min_n

An integer for the minimum number of data points in a node that are required for the node to be split further.

Value

A model specification object with the classes decision_tree_exposure and model_spec.

Details

This function is similar to parsnip::decision_tree() except that specification of an exposure column is required.

See also

parsnip::decision_tree()

Examples

parsnip::show_model_info("decision_tree_exposure")
#> Information for `decision_tree_exposure`
#>  modes: unknown, regression 
#> 
#>  engines: 
#>    regression: rpart_exposure¹
#> 
#> ¹The model can use case weights.
#> 
#>  arguments: 
#>    rpart_exposure: 
#>       cost_complexity --> cp
#>       min_n           --> minsplit
#>       tree_depth      --> maxdepth
#> 
#>  fit modules:
#>            engine       mode
#>    rpart_exposure regression
#> 
#>  prediction modules:
#>          mode         engine      methods
#>    regression rpart_exposure numeric, raw
#> 

decision_tree_exposure()
#> Poisson Decision Tree with Offsets Model Specification (regression)
#> 
#> Computational engine: rpart_exposure 
#>