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Construct a caret_list object

Source: R/caret_list.R
caret_list.Rd

Builds a list of caret::train objects, where each model corresponds to a data set in data_list. The resulting list is used as input to caret_stack() to construct a meta model.

Usage

caret_list(
  target,
  data_list,
  method,
  identifier_column_name = NULL,
  trControl = NULL,
  metric = NULL,
  trim = TRUE,
  do_parallel = TRUE,
  ...
)

Arguments

target

Target vector, either numeric for regression or a factor/character for classification.

data_list

A named list of matrix-like objects, where each element is a dataset to train a separate model on. Names are preserved in the returned caret_list and should be unique.

method

The method to train the models with. Can be a custom method or one found in caret::modelLookup().

identifier_column_name

A string giving the name of a column that links rows across datasets (e.g. a participant ID). If provided, this column must be present in all datasets in data_list for proper row matching. Use this when datasets have different numbers of rows. If NULL, datasets are assumed to have identical rows in the same order. Default is NULL.

Note: Providing identifier_column_name disables support for custom trControl. A new resampling scheme must be constructed for each dataset after row matching, which may invalidate user-supplied fold indices.

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.

trim

Logical, whether the trained models should be trimmed to save memory. Default is TRUE.

do_parallel

Logical, whether to parallelize model training across datasets. Default is TRUE.

...

Additional arguments to pass to the caret::train function (e.g. tuneGrid = tuneGrid).

Value

A caret_list object (a named list of trained caret::train models corresponding to data_list).

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
)
#> Loading required package: ggplot2
#> Loading required package: lattice

class(base_models)
#> [1] "caret_list" "list"