Hint

Swipe to navigate through the chapters of this book

2017 | OriginalPaper | Chapter

11. Performance Evaluation

Author: Miroslav Kubat

Publisher: Springer International Publishing

Published in: An Introduction to Machine Learning

» Get access to the full version

Abstract

The previous chapters pretended that performance evaluation in machine learning is a fairly straightforward matter. All it takes is to apply the induced classifier to a set of examples whose classes are known, and then count the number of errors the classifier has made. In reality, things are not as simple. Error rate rarely paints the whole picture, and there are situations in which it can even be misleading. This is why the conscientious engineer wants to be acquainted with other criteria to assess the classifiers’ performance. This knowledge will enable her to choose the one that is best in capturing the behavioral aspects of interest.

Please log in to get access to this content

To get access to this content you need the following product:

Computer Science Explorer

Purchase now

The concrete techniques employed to this end are somewhat more advanced than those discussed in this textbook, and are thus not treated here.

Some computation will be necessary if we decide to remove noisy or redundant examples.

22.

Dietterich, T. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10, 1895–1923. CrossRef

36.

Hellman, M. E. (1970). The nearest neighbor classification rule with the reject option. IEEE Transactions on Systems Science and Cybernetics, 6, 179–185. CrossRefMATH

51.

Kubat, M., Holte, R., & Matwin, S. (1997). Learning when negatives examples abound. In Proceedings of the European conference on machine learning (ECML’97), Apr 1997, Prague (pp. 146–153).

56.

Louizou, G. & Maybank, S. J. (1987). The nearest neighbor and the bayes error rates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9, 254–262. CrossRefMATH

100.

Wolpert, D. (1996). The lack of a priori distinctions between learning algorithms. Neural Computation, 8, 1341–1390. CrossRef

Title

Performance Evaluation

Book

An Introduction to Machine Learning

Print ISBN: 978-3-319-63912-3

Electronic ISBN: 978-3-319-63913-0

https://doi.org/10.1007/978-3-319-63913-0

DOI

https://doi.org/10.1007/978-3-319-63913-0_11

Author:

Miroslav Kubat

Publisher

Springer International Publishing

Sequence number

Chapter number

Chapter 11