Error Characteristic curves are a generalization of ROC curves. On the x axis of the plot there is an error tolerance and on the y axis there is a percentage of observations predicted within the given tolerance.

plotREC(object, ...)

## Arguments

object An object of class ModelAudit or modelResiduals. Other modelAudit or model Residuals objects to be plotted together.

ggplot object

## Details

REC curve estimates the Cumulative Distribution Function (CDF) of the error

Area Over the REC Curve (REC) is a biased estimate of the expected error

## References

Bi J., Bennett K.P. (2003). Regression error characteristic curves, in: Twentieth International Conference on Machine Learning (ICML-2003), Washington, DC.

plot.modelAudit, plotROC, plotRROC
library(car)
lm_au <- audit(lm_model, data = Prestige, y = Prestige$prestige) plotREC(lm_au) library(randomForest) rf_model <- randomForest(prestige~education + women + income, data = Prestige) rf_au <- audit(rf_model, data = Prestige, y = Prestige$prestige)