treesnip

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

• tree package docs
• LightGBM docs
• Catboost docs

Installation

Not on CRAN yet.

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

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	✔️	✔️	✔️
set_model_arg	✔️	✔️	✔️
set_pred	✔️	✔️	✔️
train	✔️	✔️	✔️
predict	✔️	✔️	✔️
multi_predict	⚪	✔️	✔️
tests	✔️	✔️	✔️