Supervised Learning

  1. Advanced Topics in Supervised Learning
    1. Handling Imbalanced Datasets
      1. Problem Identification
        1. Class Distribution Analysis
          1. Impact on Model Performance
          2. Sampling Techniques
            1. Oversampling Methods
              1. Random Oversampling
                1. SMOTE
                  1. ADASYN
                  2. Undersampling Methods
                    1. Random Undersampling
                      1. Edited Nearest Neighbors
                      2. Combined Sampling Methods
                      3. Algorithmic Approaches
                        1. Cost-sensitive Learning
                          1. Threshold Adjustment
                            1. Ensemble Methods for Imbalanced Data
                            2. Evaluation Considerations
                              1. Appropriate Metrics Selection
                                1. Stratified Validation
                              2. Feature Engineering Advanced Techniques
                                1. Feature Scaling Methods
                                  1. Min-Max Normalization
                                    1. Z-score Standardization
                                      1. Robust Scaling
                                        1. Unit Vector Scaling
                                        2. Encoding Categorical Variables
                                          1. One-Hot Encoding
                                            1. Implementation Details
                                              1. Handling High Cardinality
                                              2. Label Encoding
                                                1. Ordinal Relationships
                                                  1. Limitations
                                                  2. Target Encoding
                                                    1. Mean Encoding
                                                      1. Bayesian Encoding
                                                      2. Binary Encoding
                                                        1. Hashing Encoding
                                                        2. Handling Missing Data
                                                          1. Missing Data Patterns
                                                            1. Missing Completely at Random
                                                              1. Missing at Random
                                                                1. Missing Not at Random
                                                                2. Imputation Strategies
                                                                  1. Simple Imputation
                                                                    1. Mean Imputation
                                                                      1. Median Imputation
                                                                        1. Mode Imputation
                                                                        2. Advanced Imputation
                                                                          1. KNN Imputation
                                                                            1. Iterative Imputation
                                                                              1. Multiple Imputation
                                                                            2. Indicator Variables for Missingness
                                                                            3. Feature Selection Advanced Methods
                                                                              1. Univariate Selection
                                                                                1. Statistical Tests
                                                                                  1. Correlation Analysis
                                                                                  2. Recursive Feature Elimination
                                                                                    1. Algorithm Description
                                                                                      1. Cross-validation Integration
                                                                                      2. Feature Importance from Models
                                                                                        1. Tree-based Importance
                                                                                          1. Permutation Importance
                                                                                            1. SHAP Values
                                                                                          2. Dimensionality Reduction
                                                                                            1. Principal Component Analysis
                                                                                              1. Linear Discriminant Analysis
                                                                                                1. t-SNE
                                                                                                  1. UMAP
                                                                                                2. Ensemble Methods
                                                                                                  1. Bagging Methods
                                                                                                    1. Bootstrap Aggregating Concept
                                                                                                      1. Variance Reduction
                                                                                                        1. Random Forest Details
                                                                                                          1. Extra Trees
                                                                                                          2. Boosting Methods
                                                                                                            1. AdaBoost
                                                                                                              1. Adaptive Weight Adjustment
                                                                                                                1. Weak Learner Combination
                                                                                                                2. Gradient Boosting
                                                                                                                  1. Sequential Error Correction
                                                                                                                    1. Gradient Descent Analogy
                                                                                                                    2. XGBoost Advanced Features
                                                                                                                      1. Regularization Terms
                                                                                                                        1. Tree Pruning
                                                                                                                          1. Parallel Processing
                                                                                                                          2. LightGBM Optimizations
                                                                                                                            1. Histogram-based Algorithms
                                                                                                                              1. Leaf-wise Growth
                                                                                                                              2. CatBoost Innovations
                                                                                                                                1. Categorical Feature Processing
                                                                                                                                  1. Overfitting Reduction
                                                                                                                                2. Stacking and Blending
                                                                                                                                  1. Meta-learning Concept
                                                                                                                                    1. Base Learner Selection
                                                                                                                                      1. Meta-learner Training
                                                                                                                                        1. Cross-validation Predictions
                                                                                                                                          1. Hold-out Predictions
                                                                                                                                          2. Multi-level Stacking
                                                                                                                                            1. Blending vs Stacking Differences
                                                                                                                                            2. Voting Methods
                                                                                                                                              1. Hard Voting
                                                                                                                                                1. Soft Voting
                                                                                                                                                  1. Weighted Voting
                                                                                                                                                2. Hyperparameter Optimization
                                                                                                                                                  1. Search Strategies
                                                                                                                                                    1. Grid Search
                                                                                                                                                      1. Exhaustive Search
                                                                                                                                                        1. Computational Complexity
                                                                                                                                                        2. Random Search
                                                                                                                                                          1. Efficiency Benefits
                                                                                                                                                            1. Theoretical Justification
                                                                                                                                                            2. Bayesian Optimization
                                                                                                                                                              1. Gaussian Process Models
                                                                                                                                                                1. Acquisition Functions
                                                                                                                                                                  1. Sequential Model-based Optimization
                                                                                                                                                                  2. Evolutionary Algorithms
                                                                                                                                                                    1. Genetic Algorithms
                                                                                                                                                                      1. Particle Swarm Optimization
                                                                                                                                                                    2. Advanced Optimization Techniques
                                                                                                                                                                      1. Hyperband
                                                                                                                                                                        1. Population-based Training
                                                                                                                                                                          1. Neural Architecture Search
                                                                                                                                                                          2. Multi-objective Optimization
                                                                                                                                                                            1. Pareto Optimality
                                                                                                                                                                              1. Trade-off Analysis
                                                                                                                                                                            2. Model Interpretability and Explainability
                                                                                                                                                                              1. Global Interpretability
                                                                                                                                                                                1. Feature Importance
                                                                                                                                                                                  1. Partial Dependence Plots
                                                                                                                                                                                    1. Global Surrogate Models
                                                                                                                                                                                    2. Local Interpretability
                                                                                                                                                                                      1. LIME
                                                                                                                                                                                        1. Local Linear Approximation
                                                                                                                                                                                          1. Perturbation-based Explanations
                                                                                                                                                                                          2. SHAP
                                                                                                                                                                                            1. Shapley Values
                                                                                                                                                                                              1. Additive Feature Attribution
                                                                                                                                                                                                1. Different SHAP Explainers
                                                                                                                                                                                              2. Model-specific Interpretability
                                                                                                                                                                                                1. Linear Model Coefficients
                                                                                                                                                                                                  1. Tree-based Feature Importance
                                                                                                                                                                                                    1. Neural Network Attention
                                                                                                                                                                                                    2. Counterfactual Explanations
                                                                                                                                                                                                      1. What-if Analysis
                                                                                                                                                                                                        1. Actionable Insights

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                                                                                                                                                                                                    8. Model Evaluation and Validation

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                                                                                                                                                                                                    10. Practical Implementation Considerations