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

Teach regression as a complete modelling workflow rather than a single formula. This premium Unit 08 pack turns Chapter 8 of AI & Machine Learning: Volume 2 into four structured, classroom-ready lessons covering continuous prediction, Ordinary Least Squares, slope and intercept, residual analysis, multiple and polynomial regression, Ridge, Lasso and ElasticNet regularisation, model comparison, logistic regression as a classification contrast, and responsible forecasting.

Students learn to define a continuous target, interpret coefficients in context, inspect residual plots, recognise assumption violations, compare MAE, RMSE and R², identify underfitting and overfitting, and justify model choice using evidence rather than one score. European contexts such as Amsterdam housing, EU energy demand and carbon pricing connect mathematical ideas to real decisions, limitations and responsible AI practice.

The package includes a Teacher Guide, four-lesson plan, detailed Teacher Handbook, Student Summary Notes, printable Worksheet, full Answer Key, Activity Cards, Exit Tickets, a 39-slide presentation deck and a Read Me First guide. Resources support upper-secondary computing, introductory data science, AI and machine learning, vocational education, enrichment and homeschool settings.

The no-prep structure, teacher prompts, assessment guidance and printable student materials support direct teaching, group work and independent revision. The central message is simple: a trustworthy regression model is not only accurate; its errors, assumptions and limits must also be understood.

By the end of this unit, students will be able to:

• Distinguish regression from classification
• Define a continuous target and its unit
• Explain OLS using slope, intercept and residuals
• Interpret simple and multiple regression coefficients
• Read residual plots and recognise patterned errors
• Identify linearity, homoscedasticity and multicollinearity concerns
• Compare linear and polynomial regression
• Explain underfitting and overfitting
• Compare Ridge, Lasso and ElasticNet
• Interpret MAE, RMSE and R² in context
• Explain why logistic regression is normally used for classification
• Recommend a model using metrics, diagnostics and limitations
• Communicate forecasts responsibly

Lesson 1 – Regression, OLS and Residuals
Frame continuous prediction, interpret slope and intercept, and calculate residuals.

Lesson 2 – Multiple Regression and Diagnostics
Interpret conditional coefficients, examine assumptions and diagnose residual patterns.

Lesson 3 – Polynomial Regression and Regularisation
Compare model flexibility, underfitting, overfitting, Ridge, Lasso and ElasticNet.

Lesson 4 – Model Comparison and Responsible Forecasting
Evaluate models using metrics and diagnostics, contrast logistic regression and write an evidence-based recommendation.