Lesson 4: Model Validation, Interpretation, and Analytics Project
Lesson Overview
Develop practical capability in model validation, interpretation, and analytics project as part of the MATLAB for Data Analytics and Machine Learning learning path.
Topics Covered
Cross-validation
Hyperparameter optimization
Confusion matrices, ROC, residuals, and error metrics
Feature importance and explainability
Reporting and visualization
End-to-end analytics and machine-learning project
Practical Activity
Build a guided MATLAB exercise covering cross-validation.
Apply hyperparameter optimization to a practical engineering or data scenario.
Validate results and troubleshoot an issue involving end-to-end analytics and machine-learning project.
Key Takeaways
Explain and apply cross-validation.
Use confusion matrices, roc, residuals, and error metrics in a structured workflow.
Validate outputs with plots, metrics, tests, or simulation evidence.
