Classification, Regression, and Model Choice

Differentiate classification from regression and choose a simple baseline model appropriately.

The lesson is public. The pressure loop lives inside the app where submissions, revision, and AI review happen.

A simple ML pipeline with evaluation and a leakage audit.

Each lesson contributes to a week-level artifact and eventually to the shipped AI-native SaaS.

This lesson teaches the decision boundary between common supervised learning tasks and the discipline of choosing the simplest model that fits the job.

Most expensive AI mistakes begin with using the wrong class of tool. Choosing an LLM for structured prediction or choosing a complex model when a baseline would do creates operational waste.

Start with the business question, map it to target type, then choose the least complex model that gives acceptable signal and explainability.

Classification predicts categories. Regression predicts continuous values. Clustering groups without labels. The deeper lesson is that problem framing governs everything after it. If you frame the problem badly, metrics, model family, data prep, and deployment expectations all drift. A serious AI Engineer learns to slow down at framing time because that is where cost and interpretability are decided.

Predicting whether a user will churn next month is classification. Predicting next-month spend is regression. Segmenting learners into latent behavior groups is clustering. If you misuse one for another, your evaluation metric stops telling the truth.

Common failures include optimizing for novelty, skipping baselines, and choosing a model because it sounds advanced instead of because it matches the target and operating constraints.

scikit-learn Supervised Learning

Use this as the canonical task-family reference.

ML Glossary

Useful for keeping terminology precise.

Why Baselines Matter

Tie model selection to observability and practical judgment.