Complete Guide to Machine Learning Model Evaluation Methods
Core Data Concepts in Model Evaluation 📊 Training Set: Dataset used to train machine learning models (parameter optimization) Validation Set: Dataset used for hyperparameter tuning and model selection during development Test Set: Dataset reserved exclusively for assessing generalization performance → Used for final model evaluation after development completion Evaluation Methodologies Holdout Method Randomly splits the dataset into two mutually exclusive subsets: Typical split: 80% training / 20% testing (ratio varies by use case) Strengths: Computationally efficient, simple implementation Limitations: High variance in performance estimates with small datasets k-Fold Cross-Validation Systematic evaluation protocol: Partition dataset into k equal-sized folds Iteratively use each fold as validation set while training on remaining k-1 folds Aggregate results (mean ± standard deviation) across all folds Key Advantages: Reduces variance in performance estimates Maximizes data utilization (critical for small datasets) Common Variants: Stratified k-fold (preserves class distribution) Leave-One-Out Cross-Validation (LOOCV) Extreme case of k-fold where k = n (number of samples) Use Case: Small-scale datasets with <100 samples Tradeoff: Computationally prohibitive for large n (requires n model fits)