Understanding Explainability in Machine Learning

This chapter explores the critical concept of explainability in machine learning, focusing on its significance for model debugging and usability in business contexts. It discusses approaches to analyze model internals and specific data examples, emphasizing techniques such as LIME for actionable insights. The chapter highlights a structured debugging process and practical considerations in developing effective machine learning solutions, stressing the importance of feature importance and iterative improvement.