Week 7
Classification Fundamentals
Features, labels, train-test split, basic classifiers, and evaluation.
Learning goals
- Classification vs regression
- Binary and multiclass classification
- Features and labels
- Train-test split
- Accuracy, precision, recall
- Confusion matrix
Suggested classroom flow
| Session | Focus | Output |
|---|---|---|
| Day 1 | Concept introduction and guided examples | Notebook or script |
| Day 2 | Hands-on lab and teacher walkthrough | Working code |
| Day 3 | Practice tasks and discussion | Assignment draft |
| Day 4 | Assessment and mini project | Submission |
Mini project
Student Pass Predictor
Outcome for the week: students should finish with usable code, a short explanation, and at least one visual or result artifact.
Quick MCQ preview
Q1. Classification predicts?
Explanation: Classification predicts classes or labels.
Q2. Which split is commonly used to evaluate a model?
Explanation: Train-test split is standard.
Q3. Precision is most important when?
Explanation: Precision matters when false positives are costly.