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Feature Importance
Measures indicating which features contribute most to model predictions, useful for interpretation and selection.
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2 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Feature ✓ understood
A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.
- Feature Importance · you are here ✓ understood
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Evaluation SHAP SHapley Additive exPlanations - a unified approach to explaining model predictions using game theory. Evaluation LIME Local Interpretable Model-agnostic Explanations - explaining individual predictions by approximating with simpler models. Foundations Feature Selection Choosing the most relevant features from available data to reduce dimensionality and improve model performance. Shipping AI Explainability The ability to explain how an AI model makes decisions in human-understandable terms, crucial for trust and accountability. Foundations Random Forest An ensemble of decision trees trained on random subsets of data and features, reducing overfitting through averaging.