22 lines
632 B
Markdown
22 lines
632 B
Markdown
# Unit 4: Classification and Prediction
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Welcome to your simplified notes for Unit 4.
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## Table of Contents
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1. [[01_Classification_Basics|Classification Basics]]
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- Classification vs Prediction
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- Training vs Testing
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2. [[02_Decision_Trees|Decision Tree Induction]]
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- How Trees work
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- Attribute Selection (Info Gain, Gini Index)
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- Pruning
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3. [[03_Bayesian_Classification|Bayesian Classification]]
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- Bayes' Theorem
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- Naive Bayes Classifier
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4. [[04_KNN_Algorithm|K-Nearest Neighbors (KNN)]]
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- Lazy Learning
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- Distance Measures
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5. [[05_Rule_Based_Classification|Rule-Based Classification]]
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- IF-THEN Rules
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