train_models: Train Specified Machine Learning Algorithms on the Training Data

Description

Trains specified machine learning algorithms on the preprocessed training data.

Usage

train_models(
  train_data,
  label,
  task,
  algorithms,
  resampling_method,
  folds,
  repeats,
  tune_params,
  metric,
  summaryFunction = NULL,
  seed = 123,
  recipe,
  use_default_tuning = FALSE,
  tuning_strategy = "grid",
  tuning_iterations = 10,
  early_stopping = FALSE,
  adaptive = FALSE,
  algorithm_engines = NULL
)

Value

A list of trained model objects.

Arguments

train_data: Preprocessed training data frame.
label: Name of the target variable.
task: Type of task: "classification" or "regression".
algorithms: Vector of algorithm names to train.
resampling_method: Resampling method for cross-validation (e.g., "cv", "repeatedcv", "boot", "none").
folds: Number of folds for cross-validation.
repeats: Number of times to repeat cross-validation (only applicable for methods like "repeatedcv").
tune_params: List of hyperparameter tuning ranges.
metric: The performance metric to optimize.
summaryFunction: A custom summary function for model evaluation. Default is NULL.
seed: An integer value specifying the random seed for reproducibility.
recipe: A recipe object for preprocessing.
use_default_tuning: Logical indicating whether to use default tuning grids when tune_params is NULL.
tuning_strategy: A string specifying the tuning strategy ("grid", "bayes", or "none"), possibly with adaptive methods.
tuning_iterations: Number of iterations for iterative tuning methods.
early_stopping: Logical for early stopping in Bayesian tuning.
adaptive: Logical indicating whether to use adaptive/racing methods.
algorithm_engines: A named list specifying the engine to use for each algorithm.