Complex Model

Complex models in machine learning aim to achieve high predictive accuracy, but often sacrifice interpretability. Current research focuses on mitigating this trade-off through techniques like knowledge distillation (creating smaller, simpler models from larger ones), regularization methods to improve generalization, and developing model-agnostic interpretation frameworks to understand model decisions. These advancements are crucial for deploying complex models in various fields, from healthcare and finance where transparency and trust are paramount, to materials science and climate modeling where understanding underlying mechanisms is essential.