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

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")