Regression Algorithms – Complete No-Prep Teaching Unit | AI & ML Volume 2 Unit 08

Teach regression as evidence-based forecasting, not just as a formula or model name.

This complete FA Learning teaching unit gives students a clear route from defining a regression problem to interpreting residuals, comparing models and writing responsible forecast recommendations. Across four 40-minute lessons, students learn to identify the target, measurement scale, features and user; interpret residuals and metrics; question model assumptions; compare OLS, polynomial regression, Ridge, Lasso and ElasticNet; and justify model choices with evidence rather than headline scores.

The unit keeps one core habit visible throughout: a forecast is not complete until students can explain what is being predicted, how error was evaluated, why the model was chosen and what limitations remain.

Students work through target-and-scale framing, residual diagnosis, careful coefficient interpretation, polynomial flexibility, regularisation choices, model comparison and responsible reporting. The sequence also addresses common misconceptions such as treating high R-squared as proof, reading coefficients causally, assuming more complexity is always better, or confusing logistic regression with continuous regression.

Included resources:

• Teacher Guide
• 4 × 40-minute Lesson Plan
• Teacher Handbook
• Student Summary Notes
• Student Worksheet
• Full Answer Key
• Printable Activity Cards
• Exit Tickets
• 34-slide editable PowerPoint
• PDF slide version
• Read Me First guide

Teacher-facing materials include ready questions, expected answers, misconception repair, support, extension, assessment evidence and reteaching guidance. Student resources include structured written practice, model-choice tasks, residual reasoning, regularisation decisions and forecast recommendation writing.

FA Learning resources are designed to be classroom-ready, practical and evidence-focused, helping teachers move from explanation to guided practice, discussion and assessment without needing to search for additional materials.

Suitable for Artificial Intelligence, Machine Learning, Computer Science, Data Science, Python/AI enrichment and secondary or post-16 computing courses.

Key topics: Regression, OLS, residuals, MAE, RMSE, R-squared, multiple regression, polynomial regression, Ridge, Lasso, ElasticNet, regularisation, model evaluation and responsible forecasting.