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:
    1. Partition dataset into k equal-sized folds
    2. Iteratively use each fold as validation set while training on remaining k-1 folds
    3. 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)