Welcome to the InfoSci Platform
Reference Hub1
Reliability Estimates for Regression Predictions: Performance Analysis

Reliability Estimates for Regression Predictions: Performance Analysis

Zoran Bosnic, Igor Kononenko
Copyright: © 2011 |Pages: 19
ISBN13: 9781609605377|ISBN10: 1609605373|EISBN13: 9781609605384
DOI: 10.4018/978-1-60960-537-7.ch014
Cite Chapter Cite Chapter

MLA

Bosnic, Zoran, and Igor Kononenko. "Reliability Estimates for Regression Predictions: Performance Analysis." Integrations of Data Warehousing, Data Mining and Database Technologies: Innovative Approaches, edited by David Taniar and Li Chen, IGI Global, 2011, pp. 320-338. https://doi.org/10.4018/978-1-60960-537-7.ch014

APA

Bosnic, Z. & Kononenko, I. (2011). Reliability Estimates for Regression Predictions: Performance Analysis. In D. Taniar & L. Chen (Eds.), Integrations of Data Warehousing, Data Mining and Database Technologies: Innovative Approaches (pp. 320-338). IGI Global. https://doi.org/10.4018/978-1-60960-537-7.ch014

Chicago

Bosnic, Zoran, and Igor Kononenko. "Reliability Estimates for Regression Predictions: Performance Analysis." In Integrations of Data Warehousing, Data Mining and Database Technologies: Innovative Approaches, edited by David Taniar and Li Chen, 320-338. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-537-7.ch014

Export Reference

Mendeley
Favorite

Abstract

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 decisive information in the risk-sensitive applications of machine learning (e.g. medicine, engineering, and business), where they enable the users to distinguish between more and less reliable predictions. In the atuhors’ previous work they proposed eight reliability estimates for individual examples in regression and evaluated their performance. The results showed that the performance of each estimate strongly varies depending on the domain and regression model properties. In this paper they empirically analyze the dependence of reliability estimates’ performance on the data set and model properties. They present the results which show that the reliability estimates perform better when used with more accurate regression models, in domains with greater number of examples and in domains with less noisy data.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.