The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Reliable Classifications with Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Ridge Regression Confidence Machine
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Estimation of individual prediction reliability using the local sensitivity analysis
Applied Intelligence
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Transduction with confidence and credibility
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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In machine learning, the reliability estimates for individual predictions provide more information about individual prediction error than the average accuracy of predictive model (e.g. relative mean squared error). Such reliability estimates may represent a decisive information in the risk-sensitive applications of machine learning (e.g. medicine, engineering, business), where they enable the users to distinguish between better and worse predictions. In this paper, we compare the sensitivity-based reliability estimates, developed in our previous work, with four other approaches, proposed or inspired by the ideas from the related work. The results, obtained using 5 regression models, indicate the potentials for the usage of the sensitivity-based and the local modeling approach, especially with the regression trees.