Classification Algorithms – Complete No-Prep Teaching Unit | AI & ML Volume 1 Unit 07

Teach classification as evidence-based decision support rather than a list of algorithms to memorise. This Unit 07 pack transforms the final chapter of AI & Machine Learning: Foundations & Data – Volume 1 into four structured, classroom-ready lessons covering classification foundations, K-NN, decision trees, SVMs, random forests, confusion matrices, precision, recall, F1, thresholds, ROC-AUC and responsible classifier selection.

Students learn to frame a classification task clearly, separate features from labels, compare how different classifiers behave and choose evaluation evidence based on the cost of mistakes. They examine neighbour voting and scaling in K-NN, readable rule paths and overfitting in decision trees, margin and boundary reasoning in SVMs, and ensemble voting in random forests.

The unit places strong emphasis on evaluation. Students interpret false positives and false negatives, challenge accuracy-only thinking, choose between precision, recall and F1, and explain how changing a decision threshold can change the balance of errors. They finish by writing a responsible classifier recommendation that includes evidence, a limitation and a human-review condition.

Optional extension material introduces Gini impurity and pruning, support vectors, RBF kernels, C and gamma, bagging, feature importance, OOB evidence and gradient boosting. A European telecom churn case study connects classifier choice to recall, F1, ROC-AUC, error cost and human oversight.

The complete package includes:
• Teacher Guide
• Four-lesson Lesson Plan
• Teacher Handbook
• Student Summary Notes
• Student Worksheet
• Answer Key
• Activity Cards
• Exit Tickets
• Editable 40-slide PowerPoint deck
• Read Me First guide

By the end of the unit, students will be able to:
• Explain classification as category prediction
• Distinguish features, labels and predictions
• Explain K-NN neighbour voting, K choice and scaling
• Trace decision-tree rules and explain overfitting
• Describe SVM margin and boundary reasoning
• Explain random-forest ensemble voting
• Interpret confusion-matrix evidence
• Compare accuracy, precision, recall and F1
• Explain threshold trade-offs and ROC-AUC at concept level
• Compare classifiers using data shape, interpretability and error cost
• Write a cautious, evidence-based model recommendation

Lesson 1 – Classification Foundations & K-NN
Frame category prediction, identify features and labels, and explain neighbour-vote evidence.

Lesson 2 – Decision Trees
Trace readable rule paths and explain why deep trees can overfit.

Lesson 3 – SVMs & Random Forests
Compare margin/boundary reasoning with ensemble voting and interpretability trade-offs.

Lesson 4 – Evaluation, Thresholds & Responsible Selection
Use metrics, error cost and review conditions to recommend a classifier responsibly.