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Boosted Poisson Trees with Offsets

Source: R/boost_tree_offset.R
boost_tree_offset.Rd

boost_tree_offset() defines a model that creates a series of Poisson decision trees with pre-defined offsets forming an ensemble. Each tree depends on the results of previous trees. All trees in the ensemble are combined to produce a final prediction. This function can be used for count regression models only.

Usage

boost_tree_offset(
  mode = "regression",
  engine = "xgboost_offset",
  mtry = NULL,
  trees = NULL,
  min_n = NULL,
  tree_depth = NULL,
  learn_rate = NULL,
  loss_reduction = NULL,
  sample_size = NULL,
  stop_iter = 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.

mtry

A number for the number (or proportion) of predictors that will be randomly sampled at each split when creating the tree models (specific engines only).

trees

An integer for the number of trees contained in the ensemble.

min_n

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

tree_depth

An integer for the maximum depth of the tree (i.e. number of splits) (specific engines only).

learn_rate

A number for the rate at which the boosting algorithm adapts from iteration-to-iteration (specific engines only). This is sometimes referred to as the shrinkage parameter.

loss_reduction

A number for the reduction in the loss function required to split further (specific engines only).

sample_size

A number for the number (or proportion) of data that is exposed to the fitting routine. For xgboost, the sampling is done at each iteration while C5.0 samples once during training.

stop_iter

The number of iterations without improvement before stopping (specific engines only).

Value

A model specification object with the classes boost_tree_offset and model_spec.

Details

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

See also

parsnip::boost_tree()

Examples

parsnip::show_model_info("boost_tree_offset")
#> Information for `boost_tree_offset`
#>  modes: unknown, regression 
#> 
#>  engines: 
#>    regression: xgboost_offset¹
#> 
#> ¹The model can use case weights.
#> 
#>  arguments: 
#>    xgboost_offset: 
#>       tree_depth     --> max_depth
#>       trees          --> nrounds
#>       learn_rate     --> eta
#>       mtry           --> colsample_bynode
#>       min_n          --> min_child_weight
#>       loss_reduction --> gamma
#>       sample_size    --> subsample
#>       stop_iter      --> early_stop
#> 
#>  fit modules:
#>            engine       mode
#>    xgboost_offset regression
#> 
#>  prediction modules:
#>          mode         engine      methods
#>    regression xgboost_offset numeric, raw
#> 

boost_tree_offset()
#> Boosted Tree with Offsets Model Specification (regression)
#> 
#> Computational engine: xgboost_offset 
#>