Week 7: Nearest Neighbors and Naive Bayes

Overview

This week explores nearest neighbor methods for both classification and regression, efficient implementations using KD trees, and introduces Naive Bayes classification.

Learning Objectives

• Master k-nearest neighbors algorithms
• Understand data preprocessing for distance-based methods
• Implement efficient nearest neighbor search
• Apply Naive Bayes classification
• Handle various data types in these algorithms

1. Nearest Neighbors Classification

• Fundamentals
  • Algorithm principles
  • Distance metrics
  • Decision boundaries
• Implementation Considerations
  • Choosing k value
  • Z-score normalization
  • Handling nominal values
• Performance Optimization
  • Feature scaling
  • Dimensionality issues
  • Computational complexity

2. Nearest Neighbors Regression

• Core Concepts
  • Value prediction
  • Weighted averaging
  • Local approximation

3. Naive Bayes

• Theoretical Foundation
  • Bayes theorem
  • Independence assumption
  • Probability estimation
• Implementation
  • Feature likelihood
  • Prior probabilities
  • Laplace smoothing