Explain Black-Box Models

Übersicht

Ready to unlock the mystery behind your most powerful models? This Short Course was created to help data analysis professionals accomplish transparent and trustworthy AI implementation.

By completing this course, you'll master SHAP values for executive communication, systematically compare explainability methods, and align explanation strategies with stakeholder needs. By the end of this course, you will be able to:

Apply SHAP values to a black-box model and produce feature-importance visuals interpretable by non-technical executives Evaluate two XAI methods (LIME vs.

SHAP) for fidelity and stability on the same model and dataset Apply counterfactual and surrogate-model explanations to the same black-box model and compare stakeholder preference scores Evaluate explanation completeness using fidelity metrics and recommend the superior approach This course is unique because it bridges advanced explainability techniques with business communication, ensuring complex model insights drive informed decision-making. To be successful in this project, you should have a background in Python programming and machine learning fundamentals.

Lehrplan

• Module 1: SHAP Model Interpretation - Foundation
Apply SHAP values to black-box models and create executive-ready feature importance visualizations.
• Module 2: XAI Method Comparison - Core Application
Evaluate and compare LIME vs SHAP methods using fidelity and stability metrics for systematic explainability assessment.
• Module 3: Stakeholder-Centered Explanations - Integration & Assessment
Apply counterfactual and surrogate-model explanations while evaluating explanation completeness using fidelity metrics for optimal stakeholder-centered approaches.

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Unterrichtet von

Hurix Digital

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Fachgebiete

Artificial Intelligence