README.md

treesnip

This package provides the following bindings for parsnip package:

• the tree engine for decision_tree;
• the catboost engine for boost_tree - only available in catboost branch. See catboost;
• the lightGBM engine for boost_tree.

Note that the development of this package has shifted to the bonsai package. We suggest filing issues and/or pull requests there.

docs

• tree package docs
• LightGBM docs
• Catboost docs

Installation

Not on CRAN yet.

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

See catboost to use with catboost.

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

Catboost

Originally treesnip had support for both lightgbm and catboost. Since catboost has no intent to make it to CRAN we removed the parsnip implementation from the main package. You can still use it from the catboost branch that we will keep up to date with the main branch.

The catboost branch can be installed with:

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