Evaluating Learning Algorithms

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.

• Each component of machine learning evaluation is discussed separately and in great detail, before being integrated within the overall process
• For each component of machine learning evaluation, a plethora of relevant techniques are presented, that span all the techniques discussed in the machine learning literature and beyond
• All the techniques presented are illustrated using R and WEKA tools, and so the user will be able to easily adapt these illustrations to his/her own needs