rocSVM: Receiver Operating Characteristic curve (ROC curve)

In liquidSVM: A Fast and Versatile SVM Package

Description

This routine provides several points on the ROC curve by solving multiple weighted binary classification problems. It is only suitable to binary classification data.

Usage

rocSVM(x, y, ..., weight_steps = 9, do.select = TRUE)

Arguments

x	either a formula or the features
y	either the data or the labels corresponding to the features x. It can be a character in which case the data is loaded using liquidData. If it is of type liquidData then after training and selection the model is tested using the testing data (y$test).
...	configuration parameters, see Configuration. Can be threads=2, display=1, gpus=1, etc.
weight_steps	indicates how many weights between min_weight and max_weight will be used
do.select	if TRUE also does the whole selection for this model

Details

Please look at the demo-vignette (vignette('demo')) for more examples. The labels should only have value c(1,-1).

min_weight, max_weight, weight_steps: you might have to define which weighted classification problems will be considered. The choice is usually a bit tricky. Good luck ...

Value

an object of type svm. Depending on the usage this object has also $train_errors, $select_errors, and $last_result properties.

See Also

plotROC

Examples

## Not run: 
banana <- liquidData('banana-bc')
model <- rocSVM(Y ~ ., banana$train, display=1)
plotROC(model,banana$test)

## a worked example can be seen at
vignette("demo",package="liquidSVM")

## End(Not run)
 

liquidSVM documentation built on Sept. 15, 2019, 1:02 a.m.