Model Building Wisdom

Rajiv emphasizes starting with simple baselines like logistic regression before diving into deep learning models. He discusses the trade-off between interpretability and accuracy, suggesting that sometimes sacrificing interpretability for accuracy is necessary in complex models. Rajiv also highlights the importance of tools that enhance model transparency, even for neural networks.