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Bias vs. variance decomposition for regression and classification
Geurts, Pierre
2005In Maimon, O.; Rokach, L. (Eds.) Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers
 

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Keywords :
Machine learning; Statistics
Abstract :
[en] In this chapter, the important concepts of bias and variance are introduced. After an intuitive introduction to the bias/variance tradeoff, we discuss the bias/variance decompositions of the mean square error (in the context of regression problems) and of the mean misclassification error (in the context of classification problems). Then, we carry out a small empirical study providing some insight about how the parameters of a learning algorithm nfluence bias and variance.
Disciplines :
Computer science
Author, co-author :
Geurts, Pierre  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Bias vs. variance decomposition for regression and classification
Publication date :
2005
Main work title :
Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers
Editor :
Maimon, O.
Rokach, L.
Publisher :
Kluwer Academic Publishers
Available on ORBi :
since 15 October 2009

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