2024

1. Edge Case Handling and Robustness 1.1 Edge Case Detection Identification Methods Statistical Approaches Outlier detection Anomaly detection Distribution analysis Boundary cases Domain-Specific Methods Expert rules Business logic Constraint validation Historical patterns Data-Driven Detection Clustering analysis Density estimation Distance metrics Pattern recognition 1.2 Robustness Techniques Model Hardening Data Augmentation Synthetic data generation Noise injection Perturbation analysis Adversarial examples Ensemble Methods Diverse model combination Voting schemes Confidence thresholds Fallback mechanisms Defensive Programming Input validation Error handling...

1. Advanced Performance Analysis 1.1 Statistical Analysis Methods Hypothesis Testing Statistical methods to evaluate model performance claims and comparisons. Techniques: Statistical Tests McNemar’s test Wilcoxon signed-rank Student’s t-test ANOVA Confidence Intervals Bootstrap estimates Cross-validation intervals Prediction intervals Error bounds Effect Size Analysis Cohen’s d Odds ratio Risk ratio Area under curve differences Error Analysis Components: Error Decomposition Bias analysis Variance analysis Irreducible error Model complexity impact Error Distribution Error patterns Outlier impact Residual analysis Heteroscedasticity...

1. Algorithm Selection Methods 1.1 Selection Criteria Problem Characteristics Key Considerations: Data Type Structured vs. unstructured Numerical vs. categorical Time series vs. static Text, image, or mixed Dataset Size Small data considerations Big data requirements Memory constraints Processing limitations Problem Type Classification vs. regression Supervised vs. unsupervised Online vs. batch learning Single vs. multi-label Domain Requirements Interpretability needs Speed requirements Resource constraints Accuracy demands Performance Metrics Priority Key Factors: Accuracy Metrics Prediction accuracy Precision-recall balance...

1. Model Evaluation Techniques 1.1 Cross-Validation Methods K-Fold Cross-Validation A resampling method that divides data into k subsets, using each subset as a test set while training on others. Use Cases: Model selection Hyperparameter tuning Performance estimation Bias-variance analysis Model stability assessment Strengths: Robust evaluation Reduces overfitting Better use of data Handles small datasets Reliable estimates Limitations: Computationally intensive Time consuming Memory requirements Not suitable for time series Assumes i.i.d. data Stratified K-Fold A variation...