  The conformalClassification package implements Transductive Conformal
Prediction (TCP) and Inductive Conformal Prediction (ICP) for classification
problems. Conformal Prediction (CP) is a framework that complements the
predictions of machine learning algorithms with reliable measures of
confidence. TCP gives results with higher validity than ICP, however ICP is
computationally faster than TCP. The package conformalClassification is built
upon the random forest method, where votes of the random forest for each class
are considered as the conformity scores for each data point. Although the main
aim of the conformalClassification package is to generate CP errors (p-values)
for classification problems, the package also implements various diagnostic
measures such as deviation from validity, error rate, efficiency, observed
fuzziness and calibration plots. In future releases, we plan to extend the
package to use other machine learning algorithms, (e.g. support vector
machines) for model fitting.

Machine Learning;Machine Learning