Evaluating Learning Algorithms: A Classification Perspective

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.

機器學習領域已經成熟到可以應用於實際應用的程度。因此，研究人員擁有適當的工具來評估學習方法並理解其中的問題非常重要。本書探討了評估過程的各個方面，重點放在分類算法上。作者描述了幾種分類器性能評估、錯誤估計和重抽樣的技術，以及獲取統計顯著性和選擇適當評估領域的方法。他們還提出了一個統一的評估框架，並強調評估的不同組件之間具有顯著的相互關聯性和相互依賴性。本書使用R和WEKA進行示範，以提供更好的實際洞察和實施。本書針對機器學習理論和應用的研究人員，為在實際環境中進行算法性能評估提供了堅實的基礎。