Conocer los fundamentos de la interpretabilidad de modelos
Aplicar librerías para la interpretabilidad de modelos como: SHAP, Partial Dependence Plot y Permutation importance
Desarrollar modelos interpretables de Random Forest, LightGBM, etc
Este proyecto es un curso práctico y efectivo para aprender a generar modelos de Machine Learning interpretables. Se explican en profundidad diferentes técnicas de interpretabilidad de modelos como: SHAP, Partial Dependence Plot, Permutation importance, etc que nos permitirá entender el porqué de las predicciones. Gracias a esto, aprenderás a entrenar modelos Glassbox que puedas entender el porqué de sus decisiones.
Python Programming
SHAP
Machine Learning Interpretability
MAchine Learning interpretable
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introducción a la interpretabilidad de modelos en Machine Learning
Desarrollo del modelo de Machine Learning
Importancia de las variables: Permutation Importance
Efecto de las variables: Partial Dependence Plots
Entendiendo las predicciones individuales: SHAP
SHAP con LightGBM
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In a split-screen video, your instructor guides you step-by-step
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