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Base learner: tune hyperparameters and retrieve the best model

Source: R/base_learner.R
fit_base_learner.Rd

Multilayer perceptron model with different configurations of hidden units, dropout, activation, and learning rate using brulee and tidymodels. With proper settings, users can utilize graphics processing units (GPU) to speed up the training process.

Usage

fit_base_learner(
  rset = NULL,
  model = NULL,
  tune_grid_size = 10L,
  yvar = "Arithmetic.Mean",
  xvar = NULL,
  drop_vars = NULL,
  normalize = TRUE,
  metric = "rmse",
  ...
)

Arguments

rset

A space/time CV set generated from beethoven

model

The parsnip model object. Preferably generated from switch_model.

tune_grid_size

numeric(1), finetune grid size.

yvar

The target variable.

xvar

The predictor variables.

drop_vars

character vector or numeric. The variables to be dropped from the data.frame.

normalize

logical(1). If TRUE, all numeric predictors are normalized. Default is FALSE.

metric

character(1). The metric to be used for selecting the best. Must be one of "rmse", "rsq", "mae". Default = "rmse"

...

Additional arguments to be passed.

Value

The fitted workflow.

Details

LightGBM model is fitted at the defined rate (r_subsample) of the input dataset by grid or Bayesian optimization search. With proper settings, users can utilize graphics processing units (GPU) to speed up the training process.

XGBoost model is fitted at the defined rate (r_subsample) of the input dataset by grid or Bayesian optimization search. With proper settings, users can utilize graphics processing units (GPU) to speed up the training process.

Elastic net model is fitted at the defined rate (r_subsample) of the input dataset by grid search or Bayesian optimization.

  • MLP: Hyperparameters hidden_units, dropout, activation, and learn_rate are tuned. With tune_mode = "grid", users can modify learn_rate explicitly, and other hyperparameters will be predefined (56 combinations per learn_rate for mlp).

  • XGBoost: Hyperparameters mtry, ntrees, and learn_rate are tuned. With tune_mode = "grid", users can modify learn_rate explicitly, and other hyperparameters will be predefined (30 combinations per learn_rate).

  • LightGBM: Hyperparameters mtry, ntrees, and learn_rate are tuned. With tune_mode = "grid", users can modify learn_rate explicitly, and other hyperparameters will be predefined (30 combinations per learn_rate).

  • Elastic net: Hyperparameters mixture and penalty are tuned.

Tuning is performed based on random grid search (size = 10).

Note

tune package should be 1.2.0 or higher. brulee, xgboost, and lightgbm should be installed with GPU support. Grid search is not activated in this function, regardless of other parts' description.