Classification Model Explainability

Model predictions can be hard to trust if we don’t understand them. This course will teach you how to explain classification model outputs using confusion matrices, feature importance, and practical interpretability techniques.
What you'll learn

Understanding why a classification model makes certain predictions is essential for detecting unreliable outcomes – building trust, improving performance, and making informed business decisions. In this course, Classification Model Explainability, you’ll learn to interpret and communicate classification model behavior with confidence. First, you’ll explore how to detect class imbalance and its impact on model predictions using tools like confusion matrices. Next, you’ll discover which models offer built-in feature importance and how to interpret their outputs. Finally, you’ll learn how to apply advanced importance methods like Gini and permutation, and explain model behavior for ensemble models such as Random Forests and XGBoost. When you’re finished with this course, you’ll have the skills and knowledge of classification model explainability needed to evaluate, interpret, and communicate model decisions effectively in real-world projects

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