train_models_performance: Train multiple models and compare their performance

In specmine: Metabolomics and Spectral Data Analysis and Mining

Train multiple models and compare their performance

Description

Train a set of models, collect their resampling performance, optionally compute variable importance, and store fitted models and tuning summaries.

Usage

train_models_performance(
  dataset,
  models,
  column.class,
  validation,
  num.folds = 10,
  num.repeats = 10,
  tunelength = 10,
  tunegrid = NULL,
  metric = NULL,
  summary.function = "default",
  class.in.metadata = TRUE,
  compute.varimp = TRUE
)

Arguments

dataset	A dataset object.
models	A character vector with model names accepted by caret::train().
column.class	The metadata column containing the class labels.
validation	Validation method used in training.
num.folds	Number of folds used in resampling.
num.repeats	Number of repeats used in repeated resampling.
tunelength	Number of tuning levels evaluated by caret.
tunegrid	Optional list of tuning grids, one per model.
metric	Optional performance metric used for model selection.
summary.function	Summary function, or "default" to select the package default.
class.in.metadata	Logical; if TRUE, class labels are taken from metadata.
compute.varimp	Logical; if TRUE, variable importance is computed.

Value

A list containing model performance, variable importance, full tuning results, best tuning settings, optional confusion matrices, and final fitted models.

Examples

## Not run: 
datamat <- matrix(
  rnorm(24),
  nrow = 4,
  dimnames = list(paste0("v", 1:4), paste0("s", 1:6))
)
metadata <- data.frame(class = factor(c("A", "A", "A", "B", "B", "B")))
dataset <- list(data = datamat, metadata = metadata)
train_models_performance(
  dataset,
  models = c("rpart"),
  column.class = "class",
  validation = "cv",
  compute.varimp = FALSE
)

## End(Not run)

specmine documentation built on Aug. 5, 2026, 5:06 p.m.