In the context of machine learning models, the text discusses both neural networks, often deemed as "blackbox" models due to their lack of interpretability, and decision trees, known for their transparency and ease of interpretation. How do you perceive the trade$-$off between interpretability and predictive accuracy when choosing a model for a particular task? Can you think of scenarios or industries where interpretability is more crucial than predictive accuracy, and vice versa?