Factory for XGBoost SL wrappers
Create multiple configurations of XGBoost learners based on the desired combinations of hyperparameters.
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")
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. |
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.