# Unit 4: Classification and Prediction

Welcome to your simplified notes for Unit 4.

## Table of Contents

1. [[01_Classification_Basics|Classification Basics]]
   - Classification vs Prediction
   - Training vs Testing
2. [[02_Decision_Trees|Decision Tree Induction]]
   - How Trees work
   - Attribute Selection (Info Gain, Gini Index)
   - Pruning
3. [[03_Bayesian_Classification|Bayesian Classification]]
   - Bayes' Theorem
   - Naive Bayes Classifier
4. [[04_KNN_Algorithm|K-Nearest Neighbors (KNN)]]
   - Lazy Learning
   - Distance Measures
5. [[05_Rule_Based_Classification|Rule-Based Classification]]
   - IF-THEN Rules