Title from publisher's bibliographic system (viewed on 05 Oct 2015).

1. Introduction -- 2. Machine Learning and Statistics Overview -- 3. Performance Measures I -- 4. Performance Measures II -- 5. Error Estimation -- 6. Statistical Significance testing --7. Datasets and Experimental Framework --8. Recent Developments -- 9. Conclusion -- Appendix A: Statistical Tables -- Appendix B: Additional Information on the Data -- Appendix C: Two Case Studies.

The 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.