Model Evaluation

Definition

Model evaluation is the process of assessing how well a machine learning model performs on unseen data using metrics such as accuracy, precision, recall, or F1-score.

Purpose

The purpose is to validate model performance, detect overfitting, and ensure reliability before deployment. It provides evidence that models meet intended goals.

Importance

• Ensures models generalize beyond training data.
• Guides improvements in design and training.
• Helps compare competing algorithms.
• Supports regulatory and ethical accountability.

How It Works

1. Split data into training, validation, and test sets.
2. Train model on training data.
3. Evaluate predictions on test data using metrics.
4. Analyze errors and biases.
5. Iterate to improve performance.

Examples (Real World)

• Kaggle competitions: models evaluated with held-out test sets.
• Healthcare AI: models evaluated for sensitivity and specificity.
• Autonomous driving AI: evaluated with real-world driving scenarios.

References / Further Reading

• Han et al. Machine Learning: A Probabilistic Perspective. MIT Press.
• NIST AI Risk Management Framework.
• IEEE Transactions on Pattern Analysis and Machine Intelligence.
• A Beginner’s Guide To Large Language Model Evaluation