Description Usage Arguments Details Value Author(s) References See Also Examples
Function to plot the optimal training curve
1 2 3 4 5 | 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
1 2 3 4 5 6 7 8 9 10 11 12 13 | #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)
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