Construct a caret_stack object. — caret

Train an ensemble (stacked) model from the base learners in a caret_list. The ensemble is itself a caret::train model that learns to combine the predictions of the base models. By default, the meta-learner is trained on out-of-fold predictions from the resampling process, ensuring that the ensemble does not overfit to in-sample predictions. Alternatively, new datasets can be supplied via data_list and target for transfer-learning style ensembling.

Usage

caret_stack(
  caret_list,
  method,
  data_list = NULL,
  target = NULL,
  trControl = NULL,
  metric = NULL,
  ...
)

Arguments

caret_list: a caret_list object
method: The method to train the ensemble model. Can be a custom method or one found in caret::modelLookup().
data_list: A list of datasets to predict on, with each dataset matching the corresponding model in caret_list. If NULL, the out-of-fold predictions from the base models will be used. Default is NULL.
target: Target parameter vector that must be provided if predicting on a new data list. If NULL, the target vector used to train the base models will be used.
trControl: Control for use with the caret::train function. If NULL, .default_control() is used to construct a default control (5-fold cross validation) depending on the target type. Default is NULL.
metric: Metric for use with caret::train function. If NULL, .default_metric() is used to construct a default metric depending on the target type. Default is NULL.
...: Additional arguments to pass to caret::train

Value

A caret_stack object.

Examples

set.seed(42)

data(heart_failure_datasets)

data_list <-  heart_failure_datasets[c("cells", "holter", "mrna", "proteins")]

# Define hyperparameters to tune (optional)
tuneGrid <- expand.grid(alpha = 0.5, lambda = c(0.01, 0.1))

# Construct caret_list object
base_models <- caret_list(
  target = heart_failure_datasets$demo$hospitalizations,
  data_list = data_list,
  method = "glmnet",
  tuneGrid = tuneGrid
)

# Train a Random Forest stacked model on the out-of-fold predictions from the base models
stacked_model <- caret_stack(
  caret_list = base_models,
  method = "rf"
)

class(stacked_model)
#> [1] "caret_stack"