train_classifier: Train classifier

In specmine: Metabolomics and Spectral Data Analysis and Mining

Description

Train a specific classifier.

Usage

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

Arguments

dataset	list representing the dataset from a metabolomics experiment.
column.class	metadata column class.
model	model to be used in training.
validation	validation method.
num.folds	number of folds in cross validation.
num.repeats	number of repeats.
tunelength	number of levels for each tuning parameters.
tunegrid	dataframe with possible tuning values.
metric	metric used to evaluate the model's performance. Can be "Accuracy" or "ROC".
summary.function	summary function. For "ROC" the multiClassSummary function must be used.
class.in.metadata	boolean value to indicate if the class is in metadata.

Value

Returns the train result object from caret.

Examples

  ## Example of training a classifier
  library(specmine.datasets)
  data(cachexia)
  result = train_classifier(cachexia, "Muscle.loss", "pls", "cv")

specmine documentation built on Sept. 21, 2021, 5:06 p.m.