# Which Functions are Used for Model Evaluation? _Model evaluation functions measure how well ML models perform their intended tasks. Understanding these metrics is essential for building and maintaining reliable AI systems._ Model evaluation functions measure how well machine learning models accomplish their intended tasks. They're the foundation of model quality assessment—without them, you're guessing about performance. These functions are used throughout [ML model testing](/ml-model-testing) and inform ongoing [AI model performance](/ai-model-performance) measurement. For agent-specific evaluation, see [AI agent evaluation](/ai-agent-evaluation). Why evaluation matters: Models that look good in development can fail in production. Evaluation functions quantify performance, reveal weaknesses, and guide improvement. They're how you know whether a model is ready for deployment and how you detect when a deployed model is [degrading](/model-degradation). ## Classification Metrics Classification models assign inputs to categories. Evaluation compares predicted categories to actual categories. ### The Confusion Matrix All classification metrics derive from four outcomes: - **True Positive (TP)**: Correctly predicted positive - **True Negative (TN)**: Correctly predicted negative - **False Positive (FP)**: Incorrectly predicted positive (Type I error) - **False Negative (FN)**: Incorrectly predicted negative (Type II error) Different metrics weight these outcomes differently based on what matters for your use case. ### Core Metrics **Accuracy** - What it measures: Overall percentage correct - Formula: (TP + TN) / (TP + TN + FP + FN) - When to use: Balanced datasets where all errors cost the same - Limitation: Misleading with imbalanced data **Precision** - What it measures: Of positive predictions, how many were right? - Formula: TP / (TP + FP) - When to use: When false positives are costly - Example: Spam detection (false positives annoy users) **Recall (Sensitivity, True Positive Rate)** - What it measures: Of actual positives, how many were caught? - Formula: TP / (TP + FN) - When to use: When false negatives are costly - Example: Medical diagnosis (false negatives miss disease) **F1 Score** - What it measures: Harmonic mean of precision and recall - Formula: 2 × (Precision × Recall) / (Precision + Recall) - When to use: When you need to balance precision and recall - Limitation: Assumes equal importance of precision and recall **Specificity (True Negative Rate)** - What it measures: Of actual negatives, how many were correctly identified? - Formula: TN / (TN + FP) - When to use: When correctly identifying negatives matters ### Threshold-Independent Metrics **AUC-ROC (Area Under ROC Curve)** - What it measures: Model's ability to distinguish classes across all thresholds - Range: 0.5 (random) to 1.0 (perfect) - When to use: Comparing models or when operating threshold isn't fixed - Limitation: Can be optimistic with imbalanced data **AUC-PR (Area Under Precision-Recall Curve)** - What it measures: Precision-recall tradeoff across thresholds - When to use: Imbalanced datasets where positives are rare - Advantage: More informative than ROC for rare events ### Multi-Class Extensions For more than two classes: - **Macro-averaged**: Average metric across classes (treats all classes equally) - **Micro-averaged**: Aggregate TP/TN/FP/FN across classes (weights by class frequency) - **Weighted**: Weight by class frequency in ground truth ## Regression Metrics Regression models predict continuous values. Evaluation measures the difference between predictions and actual values. ### Error Metrics **Mean Absolute Error (MAE)** - What it measures: Average absolute difference between prediction and actual - Properties: Linear penalty, robust to outliers - Interpretation: On average, predictions are off by X units **Mean Squared Error (MSE)** - What it measures: Average squared difference - Properties: Quadratic penalty, penalizes large errors more - Limitation: Sensitive to outliers **Root Mean Squared Error (RMSE)** - What it measures: Square root of MSE - Advantage: Same units as target variable - When to use: When large errors are particularly costly **Mean Absolute Percentage Error (MAPE)** - What it measures: Average percentage error - Advantage: Scale-independent, interpretable - Limitation: Undefined when actual values are zero, asymmetric ### Goodness of Fit **R-squared (Coefficient of Determination)** - What it measures: Variance explained by the model - Range: 0 to 1 (can be negative for poor models) - Interpretation: 0.8 means model explains 80% of variance - Limitation: Can be gamed by adding features **Adjusted R-squared** - What it measures: R-squared penalized for number of features - Advantage: Accounts for model complexity - When to use: Comparing models with different numbers of features ## LLM and Generative Model Metrics Traditional metrics don't apply to open-ended text generation. LLM evaluation requires specialized approaches. ### Output Quality Metrics **Groundedness** - What it measures: Is output supported by provided context? - Method: Verify claims against source material - Importance: Critical for RAG applications **Faithfulness** - What it measures: Does output accurately reflect source material? - Difference from groundedness: Focus on accuracy, not just support - Method: Check for distortions or misrepresentations **Relevance** - What it measures: Does output address the query? - Challenge: Subjective, context-dependent - Method: Human evaluation or learned relevance models **Coherence** - What it measures: Is output internally consistent and logical? - Method: Check for contradictions, logical flow ### [Hallucination](/ai-hallucinations) Metrics **Factual Accuracy** - What it measures: Are stated facts correct? - Method: Verify against knowledge bases - Challenge: Requires up-to-date fact checking **Citation Accuracy** - What it measures: Do citations exist and say what's claimed? - Method: Retrieve and compare cited sources - Importance: Critical for research applications ### Safety Metrics **Toxicity Rate** - What it measures: Frequency of harmful outputs - Method: Toxicity classifiers, human review **Policy Violation Rate** - What it measures: How often outputs violate defined policies - Method: Policy classifiers, rule-based checks ## Evaluation Challenges ### Delayed Ground Truth Many real-world outcomes take time to materialize: - Loan defaults take months or years - Customer lifetime value accumulates over years - Medical outcomes may never be definitively labeled **Strategies**: - Use proxy metrics that correlate with eventual outcomes - Monitor [drift](/ai-model-drift) as an early warning - Track prediction distribution changes - Incorporate partial or provisional labels ### Class Imbalance Rare events are hard to evaluate: - Accuracy is meaningless (99% accurate by never predicting fraud) - Standard metrics can be misleading **Strategies**: - Use precision-recall metrics instead of accuracy - Track per-class performance separately - Apply appropriate sampling for evaluation - Weight metrics by business importance ### Distribution Shifts Models trained on one distribution, evaluated on another: - Cross-validation can underestimate real-world error - Historical evaluation may not predict future performance **Strategies**: - Use time-based splits for temporal data - Evaluate on out-of-distribution samples - Monitor production performance continuously - Compare evaluation and production metrics Evaluation functions inform your policies. [AI supervision](/ai-supervision) enforces them—using these metrics to determine when models can operate autonomously and when they need intervention. ## How Swept AI Approaches Evaluation Swept AI provides comprehensive model evaluation: - **[Evaluate](/product/evaluate)**: Pre-deployment assessment across accuracy, [fairness](/ai-bias-fairness), robustness, and [safety](/ai-safety). Test with the metrics that matter for your use case. - **Multi-metric tracking**: Monitor multiple evaluation functions simultaneously. Understand tradeoffs between precision and recall, accuracy and fairness. - **LLM-native evaluation**: Purpose-built metrics for language models including [hallucination](/ai-hallucinations) detection, groundedness scoring, and [safety](/ai-safety) evaluation. The right evaluation functions tell you whether your model is ready—and whether it stays ready. For robustness evaluation, see [adversarial testing](/ai-adversarial-testing). See also: [Noise Is the Real Test](/post/noise-is-the-real-test-ai-quality-assurance-needs-a-new-foundation).