Nathalie JapkowiczCambridge University Press, 3/6/2014EAN 9781107653115, ISBN10: 1107653118Paperback, 424 pages, 23.4 x 15.6 x 2.4 cmLanguage: EnglishThe field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.1. Introduction2. Machine learning and statistics overview3. Performance measures I4. Performance measures II5. Error estimation6. Statistical significance testing7. Data sets and experimental framework8. Recent developments9. ConclusionAppendix Astatistical tablesAppendix Badditional information on the dataAppendix Ctwo case studies.
