Explainability in AI and machine learning models ensures that their decisions are understandable to humans. Techniques like feature importance analysis, SHAP (Shapley Additive Explanations), and LIME (Local Interpretable Model-agnostic Explanations) help identify how specific inputs influence predictions. Visual tools like decision trees and heatmaps simplify complex models. By fostering transparency, explainability builds trust, supports ethical practices, and enables compliance with regulations. It also allows developers to debug models and refine performance effectively. To gain deeper insights into building interpretable models, consider enrolling in an AI and machine learning course, which equips learners with essential tools and techniques.
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