Evaluation of prediction reliability in regression using the transduction principle

In machine learning community there are many efforts to improve overall reliability of predictors measured as an error on the testing set. But in contrast, very little research has been done concerning prediction reliability of a single answer. This article describes an algorithm that can be used for evaluation of prediction reliability in regression. The basic idea of the algorithm is based on construction of transductive predictors. Using them, the algorithm makes inference from the differences between initial and transductive predictions to the error on a single new case. The implementation of the algorithm with regression tress managed to significantly reduce the relative mean squared error on the majority of the tested domains.