I just completed Module 3: Machine Learning for Classification from ML Zoomcamp 2026 🎉
This module covered:
🔹 Training and using logistic regression models
🔹 Encoding categorical variables with one-hot encoding
🔹 Measuring feature importance with the mutual information score
🔹 Selecting useful features through feature elimination
🔹 Interpreting what a classification model actually learned
✨ My key takeaway: correlation and mutual information answer different questions. The biggest correlation in the dataset was interaction_count × lead_score, but the feature with the highest mutual information score with the target was lead_source, a categorical feature that a correlation matrix literally cannot see. If I had ranked features by correlation alone, I would have gotten it wrong.
🤔 Something I found interesting: in the feature elimination exercise, removing number_of_courses_viewed cost 0.002 accuracy, while removing interaction_count cost (0.044). A gap that smalllooked like noise to me, so I re-ran the whole comparison with a second one-hot encoding approach to confirm it was deterministic and not an artifact of the library. It was. Small differences deserve verification before you dismiss them as noise.
➡️ Next step → Module 4: Evaluation Metrics for Classification.