Module 6: The Prediction Branch
Testing Your Theory With Data — Classification
Learning Objectives
- Build a predictive model using Random Forest (100 decision trees)
- Apply logistic regression as the interpretable baseline
- Validate models with train/test split
- Read ROC curves and confusion matrices
- Report ML classification results for publication
Topics
| Topic | Type |
|---|---|
| Decision Tree Navigator | Opening |
| Random Forest: Wisdom of the Crowd | Lecture |
| Logistic Regression: The Interpretable Baseline | Lecture |
| Train/Test Split | Lecture |
| Predicting Innovation Success | Live Demo |
| The ROC Curve: How Good Is Your Model? | Lecture |
| Confusion Matrix: Types of Errors | Lecture |
| Reporting for Publication | Lecture |
| Your Turn: Where Does This Fit? | Socratic |
| Map Check: Where Are We? | Discussion |