train_classifier: Train a classifier

In specmine: Metabolomics and Spectral Data Analysis and Mining

Train a classifier

Description

Train a classifier from a dataset object using metadata or data-derived class labels and a resampling strategy supported by caret.

Usage

train_classifier(
  dataset,
  column.class,
  model,
  validation,
  num.folds = 10,
  num.repeats = 10,
  tunelength = 10,
  tunegrid = NULL,
  metric = NULL,
  summary.function = caret::defaultSummary,
  class.in.metadata = TRUE
)

Arguments

dataset	A dataset object.
column.class	The metadata column containing the class labels.
model	A model name accepted by caret::train().
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 data frame of tuning parameter combinations.
metric	Optional performance metric used for model selection.
summary.function	Summary function passed to caret::trainControl().
class.in.metadata	Logical; if TRUE, class labels are taken from dataset$metadata, otherwise from dataset$data.

Value

A caret training object returned by caret::train().

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_classifier(dataset, "class", model = "rpart", validation = "cv")

## End(Not run)

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