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 talk (based on our upcoming book) examines various aspects of the evaluation process with an emphasis on classification algorithms. We will discuss several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. We will also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. Finally, we will discuss how the techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation.