Title: Practice and formative deliverable
Course: Advanced Data Science: A Graduate Course > Section: Module 4: Supervised learning: regression and classification

Practice and formative deliverable

The practice items below are formative and ungraded. They build intuition before the graded assessment. The formative deliverable is checked for completion and returned with feedback, and it earns completion points on submission, credited on completion rather than scored for correctness.

Before you fit anything for a grade, build visual intuition for what each model family is actually doing to the data.

1. Work through MLU-Explain Linear Regression, dragging points to feel how least squares responds to leverage and outliers.
2. Work through MLU-Explain Logistic Regression, and connect the sigmoid to the log-odds interpretation from Chapter 4.
3. Work through MLU-Explain Random Forest, watching how averaging many decorrelated trees smooths the jagged decision boundary of a single tree.
4. Open TensorFlow Playground and, using only the spiral and circle datasets, observe how a purely linear model fails where added features and capacity succeed; this is the bias–variance story made visible.
5. Read R2D3, A Visual Introduction to Machine Learning to see how a tree partitions feature space and how depth trades bias for variance. Use ONLY these URLs.

◎ Formative deliverable: On a shared dataset provided in the module folder, fit a baseline model (an intercept-only or single-feature regression for a continuous target, or a majority-class or single-feature logistic model for a label) and one stronger model (a full multiple regression, or a tuned random forest). Report a single held-out metric for each (RMSE or MAE for regression; ROC-AUC or F1 for classification, chosen and justified for the target), and write two sentences explaining why the stronger model does or does not help on this data. A stronger model that fails to beat the baseline is a legitimate and informative result, say so.

“Advanced Data Science by Michelle Blomberg is licensed under CC BY-NC-SA 4.0.”