Model Explainability Tools

Model explainability tools are software or frameworks designed to provide insights into how machine learning models make predictions. These tools help users understand the decision-making process of complex models, particularly in deep learning, where the inner workings can be opaque. Common characteristics include visualizations, feature importance analysis, and sensitivity analysis. Use cases often involve regulatory compliance, improving model transparency, and building trust with stakeholders by elucidating model behavior in real-world applications.