Navigating Model Selection with Visualization

This chapter delves into the machine learning process, focusing on the model selection triple of feature analysis, algorithm selection, and hyperparameter tuning. It emphasizes the importance of using visual tools like Yellowbrick to enhance model interpretability and guide informed decision-making. Additionally, the chapter discusses tracking model performance decay through visualization and the significance of tailored metrics in evaluating and optimizing models.