movieROC: Visualizing the Decision Rules Underlying Binary Classification
Visualization of decision rules for binary classification and Receiver Operating Characteristic (ROC) curve estimation under different generalizations:
- making the classification subsets flexible to cover those scenarios where both extremes of the
marker are associated with a higher risk of being positive, considering two thresholds
(gROC curve);
- transforming the marker by a function either defined by the user or resulting from a logistic
regression model (hROC curve);
- considering a linear transformation with some fixed parameters introduced by the user,
dynamic parameters or empirically maximizing TPR for each FPR for a bivariate marker.
Also a quadratic transformation with particular coefficients or a function fitted by a logistic
regression model can be considered (biROC curve);
- considering a linear transformation with some fixed parameters introduced by the user,
dynamic parameters or a function fitted by a logistic regression model (multiROC curve).
The classification regions behind each point of the ROC curve are displayed in both fixed
graphics (plot.buildROC(), plot.regions() or plot.funregions() function) or videos (movieROC()
function).
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