Supervised Learning

  1. Instance-Based Learning
    1. K-Nearest Neighbors
      1. Algorithm Overview
        1. Training Phase Characteristics
          1. Prediction Phase Process
          2. Choosing the Value of K
            1. Odd vs Even K Values
              1. Bias-Variance Implications
                1. Cross-validation for K Selection
                2. Distance Metrics
                  1. Euclidean Distance
                    1. Mathematical Definition
                      1. Geometric Interpretation
                      2. Manhattan Distance
                        1. L1 Norm
                          1. City Block Distance
                          2. Minkowski Distance
                            1. Generalized Distance Metric
                              1. Parameter p Effects
                              2. Cosine Similarity
                                1. Hamming Distance
                                  1. Custom Distance Measures
                                  2. Weighted KNN
                                    1. Distance-based Weighting
                                      1. Inverse Distance Weighting
                                      2. Computational Considerations
                                        1. Time Complexity
                                          1. Space Complexity
                                            1. Curse of Dimensionality
                                            2. Feature Scaling Importance
                                              1. Impact of Different Scales
                                                1. Normalization Strategies
                                                2. Advantages and Limitations
                                                  1. Non-parametric Nature
                                                    1. Local Decision Boundaries
                                                      1. Computational Efficiency Issues
                                                        1. Sensitivity to Irrelevant Features

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                                                    4. Tree-Based Models

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                                                    6. Support Vector Machines