Produces two plots for model evaluation. The first plot shows the Receiver Operating Characteristic (ROC)-curves, the other the Precision-recall (PR)-curves for the different cross-validation repetitions.

model.evaluation.plot(..., fn.plot = NULL,
    colours=NULL, show.all=FALSE, verbose = 1)



one or more object of class siamcat-class, can be named


string, filename for the pdf-plot


colour specification for the different siamcat-class- objects, defaults to NULL which will cause the colours to be picked from the 'Set1' palette


boolean, Should all repeated cross-validation models be plotted?


control output: 0 for no output at all, 1 for only information about progress and success, 2 for normal level of information and 3 for full debug information, defaults to 1


Does not return anything, but produces the model evaluation plot.


data(siamcat_example) # simple working example model.evaluation.plot(siamcat_example, fn.plot='./eval.pdf')
#> Plotted evaluation of predictions successfully to: ./eval.pdf
# plot several named SIAMCAT object # (although we use only one example object here) model.evaluation.plot('Example_1'=siamcat_example, 'Example_2'=siamcat_example, colours=c('red', 'blue'), fn.plot='./eval.pdf')
#> Plotted evaluation of predictions successfully to: ./eval.pdf