train_catboost: Boosted trees with catboost

In tidymodels/bonsai: Model Wrappers for Tree-Based Models

Boosted trees with catboost

Description

train_catboost is a wrapper for catboost tree-based models where all of the model arguments are in the main function.

Usage

train_catboost(
  x,
  y,
  weights = NULL,
  iterations = 1000,
  learning_rate = 0.03,
  depth = 6,
  l2_leaf_reg = 3,
  random_strength = 1,
  bagging_temperature = 1,
  rsm = 1,
  quiet = TRUE,
  ...
)

Arguments

x	A data frame of predictors.
y	A vector (factor or numeric) or matrix (numeric) of outcome data.
weights	A numeric vector of sample weights, defaults to NULL.
iterations	The maximum number of trees that can be built when solving machine learning problems. Default to 1000.
learning_rate	A positive numeric value for the learning rate. Defaults to 0.03.
depth	An integer for the depth of the trees. Default to 6.
l2_leaf_reg	A numeric value for the L2 regularization coefficient. Used for leaf value calculation. Defaults to 3.
random_strength	The amount of randomness to use for scoring splits when the tree structure is selected. Use this parameter to avoid overfitting the model. Defaults to 1.
bagging_temperature	A numeric value, controls intensity of Bayesian bagging. The higher the temperature the more aggressive bagging is. Defaults to 1.
rsm	A numeric value between 0 and 1, random subspace method. The percentage of features to use at each iteration of building trees. At each iteration, features are selected over again at random. Defaults to 1.
quiet	A logical; should logging by catboost::catboost.train() be muted?
...	Other options to pass to catboost::catboost.train(). Arguments will be correctly routed to the param argument, or as a main argument, depending on their name.

Details

This is an internal function, not meant to be directly called by the user.

Value

A fitted catboost.Model object.

Source

https://catboost.ai/docs/en/references/training-parameters/.

tidymodels/bonsai documentation built on July 3, 2025, 7:35 p.m.