CostCurves: CostCurves
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 cost curves using different threshold choice methods

CostCurves(predictions, classes, cost_lines = TRUE,
                 test_optimal = TRUE, train_optimal = FALSE,
                 predictionsT = NULL, classesT = NULL,
                 uniquec=FALSE, uniqueTrain=FALSE, uniquecT=FALSE,
                 score_driven = FALSE, rate_driven = FALSE,
                 kendall_curves = FALSE, loss2skew = FALSE,
                 hold = FALSE, gridOFF = TRUE, pointsOFF = TRUE,
                 legendOFF = FALSE,
                 main, xlab, ylab, namesClassifiers, col, lwd,
                 lty, pch, cex,xPosLegend,yPosLegend, cexL)

`predictions`	A list with predicted scores
`classes`	A list with labels, (only binary classes).
`cost_lines`	If TRUE Lines cost are displayed
`test_optimal`	Plot cost curves using test optimal threshold choice method.
`train_optimal`	Plot cost curves using train optimal threshold choice method.
`predictionsT`	A list with predicted scores. Required when option train_optimal is TRUE.
`classesT`	A list with labels, (only binary classes). Required when option train_optimal is TRUE.
`uniquec`	If TRUE, the same labels are used for each array in a list predictions.
`uniquecT`	If TRUE, the same array classes is used for each array in a list train predictions. Required when option train_optimal is TRUE.
`uniqueTrain`	If TRUE, the same array of predictionsT and classesT is used for each array in a list predictions. It's necessary only if the option train_optimal is TRUE.
`score_driven`	If TRUE, plot the cost curves using the score driven threshold choice method.
`rate_driven`	If TRUE, plot the cost curves the using rate driven threshold choice method.
`kendall_curves`	If TRUE, plot cost curves using kendall driven threshold choice method.
`loss2skew`	If TRUE, loss by Skew is plotted otherwise loss by cost is plotted.
`hold`	If it is TRUE, the plot is maintained open allowing to plot new curves on the same plot.
`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.
`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 point. It is posible select any valid option to the graphics parameters of R.

This function allows to plot cost curves considering different threshold choice method.

Cost Lines: CostLines

Test Optimal threshold choice method: TestOptimal

Train Optimal threshold choice method: TrainOptimal

Score Driven threshold choice method: BrierCurves

Rate Driven threshold choice method: RateDrivenCurves

Kendall Curves: KendallCurves

A list of arrays with AUCCs of different cost curves selected.

Paulina Morillo: paumoal@inf.upv.es

Drummond, C., & Holte, R. C. (2006). Cost curves: An improved method for visualizing classifier performance.

Fawcett, T. (2006). An introduction to ROC analysis. Pattern recognition letters, 27(8), 861-874.

Ferri, C., Hernandez-orallo, J., & Flach, P. A. (2011). Brier curves: a new cost-based visualisation of classifier performance. In Proceedings of the 28th International Conference on Machine Learning (ICML-11) (pp. 585-592).

Hernandez-Orallo, J., Flach, P., & Ferri, C. (2013). ROC curves in cost space. Machine learning, 93(1), 71-91.

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

#Load data
data(predictions)

#Loss by Skew
R<-CostCurves(list(predictions$A, predictions$B), 
	list(predictions$classes), uniquec = TRUE, train_optimal = TRUE,
	predictionsT =  list(predictions$B, predictions$A),
	classesT = list(predictions$clases, predictions$classes),
	loss2skew = TRUE, test_optimal = FALSE,
	rate_driven = FALSE, col=list(c("cyan", "red"), c("gray", "blue")),
	pointsOFF = FALSE, cex=1)

R<-CostCurves(list(predictions$B), list(predictions$classes), 
	rate_driven = TRUE, kendall_curves = TRUE, col=c("gray", "red","green"))

#Loss by Cost
R<-CostCurves(list(predictions$A, predictions$B), list(predictions$classes), 
	uniquec = TRUE,	train_optimal = TRUE,
	predictionsT =  list(predictions$B, predictions$A),
	classesT = list(predictions$classes), uniquecT = TRUE)

R<-CostCurves(list(predictions$A, predictions$B), list(predictions$classes),
	uniquec = TRUE, train_optimal = TRUE, predictionsT =  list(predictions$B),
	classesT = list(predictions$classes), uniqueTrain = TRUE,
	kendall_curves = TRUE)