conformalClassification: A Conformal Prediction R Package for Classification

Conformal predictors are confidence predictors that result in prediction sets for all confidence levels. Thus, Conformal Prediction (CP) is a framework that complements the predictions of machine learning algorithms with reliable measures of confidence. Transductive Conformal Prediction (TCP) works in an online transductive setting, such that learning and prediction occur simultaneously. In this sense confidence in a prediction is tailored both to the previously seen objects (whose features and labels are known) and to the features of the new object, whose label is to be predicted. By conditioning on the new objects conformal predictors take account of how difficult a particular object is to label and adjust their confidence in the prediction accordingly, as opposed to having an overall error rate for labelling all new objects Vovk et al. (2005).