Understand and implement model interpretability techniques using SHAP and LIME. Learn to explain predictions from complex machine learning models, building trust and enabling debugging. Covers theoretical foundations and practical application in Python.
Prerequisites Python & ML Basics
Level:
Interpretability Concepts
Distinguish between interpretability and explainability, and understand why they are necessary for machine learning models.
LIME Implementation
Apply the LIME algorithm to explain individual predictions of various machine learning models.
SHAP Implementation
Utilize the SHAP framework and its different explainers to generate local and global explanations for model behavior.
Explanation Evaluation
Understand how to interpret and compare the outputs generated by LIME and SHAP.
Practical Application
Integrate interpretability techniques into a standard machine learning workflow using Python libraries.