This package provides the following bindings for parsnip package:

  • the tree engine for decision_tree;
  • the catboost engine for boost_tree;
  • the lightGBM engine for boost_tree.

docs

Installation

Not on CRAN yet.

remotes::install_github("curso-r/treesnip")

Hint: for easy lightgbm installation, check the {rightgbm} package.

devtools::install_github("curso-r/rightgbm")
rightgbm::install_lightgbm()

Minimal Example

# decision_tree
model <- parsnip::decision_tree()
parsnip::set_engine(model, "tree")

# boost_tree
model <- parsnip::boost_tree(mtry = 1, trees = 50)

parsnip::set_engine(model, "catboost")
parsnip::set_engine(model, "lightgbm")

Hyperparameters map

decision_tree()

parsnip

tree

min_n

minsize

cost_complexity

mindev

boost_tree()

parsnip

catboost

lightGBM

mtry

rsm

feature_fraction

trees

iterations

num_iterations

min_n

min_data_in_leaf

min_data_in_leaf

tree_depth

depth

max_depth

learn_rate

learning_rate

learning_rate

loss_reduction

Not found

min_gain_to_split

sample_size

subsample

bagging_fraction

Roadmap

fun

tree

catboost

lightGBM

set_fit

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set_model_arg

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set_pred

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train

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predict

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multi_predict

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tests

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