train_models {fastml} | 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
)
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 |
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 |
Value
A list of trained model objects.