Data Mining and Knowledge Discovery

  1. Classification Methods
    1. Classification Fundamentals
      1. Supervised Learning Concepts
        1. Training and Testing Paradigms
          1. Class Label Prediction
            1. Performance Evaluation Basics
              1. Overfitting and Underfitting
                1. Bias-Variance Tradeoff
                2. Decision Tree Learning
                  1. Tree Construction Algorithms
                    1. Hunt's Algorithm
                      1. ID3 Algorithm
                        1. C4.5 Algorithm
                          1. CART Algorithm
                          2. Splitting Criteria
                            1. Information Gain
                              1. Gain Ratio
                                1. Gini Index
                                  1. Chi-Square Test
                                  2. Tree Pruning Methods
                                    1. Pre-Pruning Strategies
                                      1. Post-Pruning Techniques
                                        1. Reduced Error Pruning
                                          1. Cost Complexity Pruning
                                          2. Handling Special Cases
                                            1. Continuous Attributes
                                              1. Missing Values
                                                1. Multi-Way Splits
                                              2. Probabilistic Classification
                                                1. Bayesian Learning Theory
                                                  1. Naive Bayes Classifier
                                                    1. Gaussian Naive Bayes
                                                      1. Multinomial Naive Bayes
                                                        1. Bernoulli Naive Bayes
                                                        2. Bayesian Networks
                                                          1. Network Structure Learning
                                                            1. Parameter Learning
                                                              1. Inference Algorithms
                                                            2. Instance-Based Learning
                                                              1. K-Nearest Neighbors Algorithm
                                                                1. Distance Metrics
                                                                  1. Euclidean Distance
                                                                    1. Manhattan Distance
                                                                      1. Minkowski Distance
                                                                        1. Cosine Similarity
                                                                        2. Neighborhood Selection
                                                                          1. Weighted Voting Schemes
                                                                            1. Computational Optimization
                                                                            2. Linear Classification Methods
                                                                              1. Linear Discriminant Analysis
                                                                                1. Logistic Regression
                                                                                  1. Binary Classification
                                                                                    1. Multi-Class Extensions
                                                                                      1. Regularization Techniques
                                                                                      2. Perceptron Algorithm
                                                                                        1. Linear Support Vector Machines
                                                                                        2. Support Vector Machines
                                                                                          1. Maximum Margin Principle
                                                                                            1. Linear SVM Formulation
                                                                                              1. Soft Margin SVM
                                                                                                1. Non-Linear SVM
                                                                                                  1. Kernel Functions
                                                                                                    1. Polynomial Kernels
                                                                                                      1. Radial Basis Function Kernels
                                                                                                        1. String Kernels
                                                                                                        2. Multi-Class SVM Extensions
                                                                                                        3. Rule-Based Classification
                                                                                                          1. Rule Induction Methods
                                                                                                            1. Rule Representation
                                                                                                              1. Rule Evaluation Metrics
                                                                                                                1. Rule Pruning Techniques
                                                                                                                  1. Rule Ordering Strategies
                                                                                                                  2. Ensemble Learning Methods
                                                                                                                    1. Ensemble Principles
                                                                                                                      1. Bagging Methods
                                                                                                                        1. Bootstrap Aggregating
                                                                                                                          1. Random Forests
                                                                                                                            1. Extra Trees
                                                                                                                            2. Boosting Algorithms
                                                                                                                              1. AdaBoost
                                                                                                                                1. Gradient Boosting
                                                                                                                                  1. XGBoost
                                                                                                                                    1. LightGBM
                                                                                                                                    2. Stacking Approaches
                                                                                                                                      1. Voting Classifiers

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                                                                                                                                    3. Data Preprocessing Fundamentals

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                                                                                                                                    5. Regression Analysis