README.md

In curso-r/treesnip: Tree-Based Model Engines for 'parsnip'

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")

curso-r/treesnip documentation built on May 7, 2022, 1:10 a.m.