TrainOptimal: Plotting Cost Curves using the Optimal Training threshold...
In paumoal/costcurvespkg: Display and analyze Cost Curves and treshold choice

Description Usage Arguments Details Value Author(s) References See Also Examples

Function to plot the optimal training curve

TrainOptimal(predictions_train, classes_train, predictions_test, classes_test,
                uniquecT=FALSE, uniquect=FALSE, refuseT=FALSE, loss2skew=FALSE,
                hold=FALSE, plotOFF=FALSE, pointsOFF=TRUE, gridOFF=TRUE,legendOFF=FALSE,
                main, xlab, ylab, namesClassifiers,namesTests, lwd, lty, col,pch, 
                cex, xPosLegend,yPosLegend, cexL)

`predictions_train`	A list with predicted scores. Train Dataset
`classes_train`	A list with labels, (only binary classes). Train Dataset
`predictions_test`	A list with predicted scores. Train Dataset
`classes_test`	A list with labels, (only binary classes). Test Dataset
`uniquecT`	If it is TRUE, the same array classes is used for each array in a list predictions Training.
`uniquect`	If it is TRUE, the same array classes is used for each array in a list predictions test.
`refuseT`	It is possible to use the same training classifier for every test.
`loss2skew`	If it is TRUE, loss by Skew is plotted otherwise loss by cost is plotted.
`hold`	If it is TRUE, the plot is maintained open allowing to insert new curves on the same plot.
`plotOFF`	Disable/enable plot visualization, only return AUC values.
`gridOFF`	Disable/enable grid visualization.
`pointsOFF`	Disable/enable point marks visualization.
`legendOFF`	Disable/enable legend visualization.
`main`	title.
`xlab`	x label.
`ylab`	y label.
`namesClassifiers`	An array with names of each classifier.
`namesTests`	An array with names of each classifier.
`lwd`	Line width.
`lty`	Line type.
`col`	Line color.
`pch`	Point type.
`cex`	Size point.
`xPosLegend`	x coordinate to be used to position the legend.
`yPosLegend`	y coordinate to be used to position the legend.
`cexL`	size of box legend.

This function plot the cost curves on a test considering the optimal threshold on a train dataset.

An array with AUCC (Area Under Cost Curve) for each test.

Paulina Morillo: paumoal@inf.upv.es

#It reference is not exact. Hernandez-Orallo, J., Flach, P., & Ferri, C. (2013). ROC curves in cost space. Machine learning, 93(1), 71-91.

BrierCurves, CostCurves, CostLines, KendallCurves, predictions, RateDrivenCurves, TestOptimal, TP_FP.rates

#Load data
data(predictions)

#Loss by Skew
R<-TrainOptimal(list(predictions$A), list(predictions$classes), 
	list(predictions$A, predictions$B), list(predictions$classes), 
	uniquect = TRUE, uniquecT = TRUE, loss2skew = TRUE, refuseT = TRUE)

#Loss by Cost
R<-TrainOptimal(list(predictions$A, predictions$B), list(predictions$classes),
	list(predictions$B, predictions$A), list(predictions$classes), uniquect = TRUE,
	namesClassifiers=c("A", "B"), namesTests=c("B","A"), uniquecT = TRUE)