Overview
Model evaluation measures how well a machine learning model performs its intended task and whether it generalizes beyond training data. Proper evaluation requires choosing appropriate metrics, using held-out test sets, and understanding the failure modes specific to the problem domain. For classification tasks, accuracy alone is often misleading — precision, recall, and F1 scores provide clearer signal, especially with imbalanced classes. For generative AI, evaluation becomes harder: automated metrics like BLEU and ROUGE capture surface-level quality, but human evaluation remains necessary for assessing coherence, factuality, and usefulness.
Key Concepts
- Train/validation/test split — Data divided into training (model learns), validation (hyperparameter tuning), and test (final evaluation) sets. Test set must remain untouched until final evaluation.
- Cross-validation — Rotating which data serves as validation across multiple folds. More robust than a single split, especially for small datasets.
- Precision and recall — Precision: fraction of positive predictions that are correct. Recall: fraction of actual positives the model catches. Often in tension.
- F1 score — Harmonic mean of precision and recall. Useful when both false positives and false negatives matter.
- AUC-ROC — Area Under the Receiver Operating Characteristic curve. Measures model’s ability to discriminate between classes across all thresholds.
- LLM evaluation — Newer techniques for evaluating language models: LLM-as-judge (using one model to evaluate another), human preference ratings, and task-specific benchmarks.
- Overfitting detection — Comparing training vs validation performance. Diverging curves signal the model is memorizing rather than learning.
Trade-offs
- Automated metrics are scalable but may not capture real-world quality
- Human evaluation is gold-standard but expensive and slow
- More evaluation data means less training data (in small datasets)
- Domain-specific metrics are more useful but harder to standardize
Related
- Overfitting is the primary failure mode that evaluation detects
- Supervised Learning requires labeled test data for evaluation
- MLOps includes continuous evaluation in production pipelines