Conformal Prediction for Reliable Machine Learning

Preface

Reliable estimation of prediction confidence remains a significant challenge in machine learning, as learning algorithms proliferate into difficult real-world pattern recognition applications. The Conformal Predictions (CP) framework is a recent development in machine learning to associate reliable measures of confidence with pattern recognition settings including classification, regression, and clustering. This framework is an outgrowth of earlier work on algorithmic randomness, transductive inference, and hypothesis testing, and has several desirable properties for potential use in various real-world applications. One of the desirable features of this framework is the calibration of the obtained confidence values in an online setting. ...