The conformalClassification package implements Transductive Conformal Prediction (TCP) and Inductive Conformal Prediction (ICP) for classification problems.
Currently, the pakcage is built upon random forests method, where voting of random forests for each class is considered as a conformity scores for each data point. Mainly the package generates conformal prediction errors (p-values) for classification problems, it also provides various diagnostic measures such as deviation from alidity, error rate, efficiency, observed fuzziness and calibration plots. In future releases, we plan to extend package to use other machine learning algorithms, (i.e. support vector machine) for model fitting.
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