Lesson 4: Validation, Explainability, Reporting, and Deployment
Lesson Overview
Develop practical capability in validation, explainability, reporting, and deployment as part of the MATLAB for Data Analysis and Predictive Modeling learning path.
Topics Covered
Cross-validation
Confusion matrices, ROC curves, RMSE, and MAE
Predictor importance and partial dependence
Model risk and overfitting
Generating reusable prediction code
Final predictive-modeling project
Practical Activity
Build a guided MATLAB exercise covering cross-validation.
Apply confusion matrices, roc curves, rmse, and mae to a practical engineering or data scenario.
Validate results and troubleshoot an issue involving final predictive-modeling project.
Key Takeaways
Explain and apply cross-validation.
Use predictor importance and partial dependence in a structured workflow.
Validate outputs with plots, metrics, tests, or simulation evidence.
