Description Usage Arguments Value Examples
View source: R/machinelearning.R
Train a specific classifier.
1 2 3 4 | 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)
|
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. |
Returns the train result object from caret.
1 2 3 4 | ## Example of training a classifier
library(specmine.datasets)
data(cachexia)
result = train_classifier(cachexia, "Muscle.loss", "pls", "cv")
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