Reference : Bias vs. variance decomposition for regression and classification
Parts of books : Contribution to collective works
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/25734
Bias vs. variance decomposition for regression and classification
English
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2005
Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers
Maimon, O.
Rokach, L.
Kluwer Academic Publishers
[en] Machine learning ; Statistics
[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.
http://hdl.handle.net/2268/25734
http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2005/Geu05

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Restricted access
geurts-biasvar.pdfAuthor preprint116.7 kBRequest copy

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.