create.SL.xgboost: Factory for XGBoost SL wrappers

In SuperLearner: Super Learner Prediction

Factory for XGBoost SL wrappers

Description

Create multiple configurations of XGBoost learners based on the desired combinations of hyperparameters.

Usage

create.SL.xgboost(tune = list(ntrees = c(1000), max_depth = c(4), shrinkage =
  c(0.1), minobspernode = c(10)), detailed_names = F, env = .GlobalEnv,
  name_prefix = "SL.xgb")

Arguments

tune	List of hyperparameter settings to test. If specified, each hyperparameter will need to be defined.
detailed_names	Set to T to have the function names include the parameter configurations.
env	Environment in which to create the SL.xgboost functions. Defaults to the global environment.
name_prefix	The prefix string for the name of each function that is generated.

Examples

# Create a new environment to store the learner functions.
# This keeps the global environment organized.
sl_env = new.env()
# Create 2 * 2 * 1 * 3 = 12 combinations of hyperparameters.
tune = list(ntrees = c(100, 500), max_depth = c(1, 2), minobspernode = 10,
            shrinkage = c(0.1, 0.01, 0.001))
# Generate a separate learner for each combination.
xgb_grid = create.SL.xgboost(tune = tune, env = sl_env)
# Review the function configurations.
xgb_grid
# Attach the environment so that the custom learner functions can be accessed.
attach(sl_env)
## Not run: 
sl = SuperLearner(Y = Y, X = X, SL.library = xgb_grid$names)

## End(Not run)
detach(sl_env)

SuperLearner documentation built on May 29, 2024, 5:25 a.m.