train_models: Train Specified Machine Learning Algorithms on the Training...
In fastml: Fast Machine Learning Model Training and Evaluation

train_models

R Documentation

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
)

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.