Explicar Modelos de Caja Negra

Resumen

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.

Programa

• Módulo 1: Interpretación del Modelo SHAP - Fundamentos
Aplicar valores SHAP a modelos de caja negra y crear visualizaciones de importancia de características listas para ejecutivos.
• Módulo 2: Comparación de Métodos XAI - Aplicación Central
Evaluar y comparar los métodos LIME vs SHAP utilizando métricas de fidelidad y estabilidad para una evaluación sistemática de explicabilidad.
• Módulo 3: Explicaciones Centradas en los Interesados - Integración y Evaluación
Aplicar explicaciones contrafactuales y de modelos sustitutos mientras se evalúa la completitud de la explicación utilizando métricas de fidelidad para enfoques óptimos centrados en los interesados.

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Impartido por

Hurix Digital

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Materias

Artificial Intelligence