Machine Learning Model Interpretability Toolkit

Develop a comprehensive toolkit for scientific machine learning model interpretability that provides multi-dimensional explainability across different model architectures. Create visualization modules that can generate feature importance maps, decision boundary representations, and uncertainty quantification for complex neural network and ensemble models. Implement statistical and information-theoretic methods for model transparency that work across domains like biology, physics, and climate science.