Homework 12: Introductory Machine Learning

Train and evaluate your first supervised model with scikit-learn. You will prepare features and labels, hold out test data, fit a model, and judge its predictions against a sensible baseline — building the intuition for why held-out data is necessary and what overfitting looks like when it happens.

Related session: Session 13 — Introductory Machine Learning

Topics Covered

• Supervised learning as learning a mapping from features to labels
• Classification versus regression
• The scikit-learn workflow: prepare, split, fit, predict, evaluate
• Why held-out data is necessary; comparing against a baseline
• Accuracy, error, and overfitting intuition

Instructions

The detailed tasks, dataset, and submission instructions for this assignment have not been posted yet. Please check back after the November 18 session.