Model Evaluator

Model Evaluator

Overview

Provides comprehensive, unbiased model evaluation following ML best practices. Goes beyond simple accuracy to evaluate models across multiple dimensions, ensuring confident deployment decisions.

Core Evaluation Framework

1. Classification Metrics

• Accuracy, Precision, Recall, F1-score
• ROC AUC, PR AUC
• Confusion matrix
• Per-class metrics (for multi-class)
• Class imbalance handling

2. Regression Metrics

• RMSE, MAE, MAPE
• R² score, Adjusted R²
• Residual analysis
• Prediction interval coverage

3. Ranking Metrics (Recommendations)

• Precision@K, Recall@K
• NDCG@K, MAP@K
• MRR (Mean Reciprocal Rank)
• Coverage, Diversity

4. Statistical Validation

• Cross-validation (K-fold, stratified, time-series)
• Confidence intervals
• Statistical significance testing
• Calibration curves

Usage

from specweave import ModelEvaluator

evaluator = ModelEvaluator(
    model=trained_model,
    X_test=X_test,
    y_test=y_test,
    increment="0042"
)

# Comprehensive evaluation
report = evaluator.evaluate_all()

# Generates:
# - .specweave/increments/0042.../evaluation-report.md
# - Visualizations (confusion matrix, ROC curves, etc.)
# - Statistical tests

Evaluation Report Structure

# Model Evaluation Report: XGBoost Classifier

## Overall Performance
- **Accuracy**: 0.87 ± 0.02 (95% CI: [0.85, 0.89])
- **ROC AUC**: 0.92 ± 0.01
- **F1 Score**: 0.85 ± 0.02

## Per-Class Performance
| Class   | Precision | Recall | F1   | Support |
|---------|-----------|--------|------|---------|
| Class 0 | 0.88      | 0.85   | 0.86 | 1000    |
| Class 1 | 0.84      | 0.87   | 0.86 | 800     |

## Confusion Matrix
[Visualization embedded]

## Cross-Validation Results
- 5-fold CV accuracy: 0.86 ± 0.03
- Fold scores: [0.85, 0.88, 0.84, 0.87, 0.86]
- No overfitting detected (train=0.89, val=0.86, gap=0.03)

## Statistical Tests
- Comparison vs baseline: p=0.001 (highly significant)
- Comparison vs previous model: p=0.042 (significant)

## Recommendations
✅ Deploy: Model meets accuracy threshold (>0.85)
✅ Stable: Low variance across folds
⚠️  Monitor: Class 1 recall slightly lower (0.84)

Model Comparison

from specweave import compare_models

models = {
    "baseline": baseline_model,
    "xgboost": xgb_model,
    "lightgbm": lgbm_model,
    "neural-net": nn_model
}

comparison = compare_models(
    models,
    X_test,
    y_test,
    metrics=["accuracy", "auc", "f1"],
    increment="0042"
)

Output:

Model Comparison Report
=======================

| Model      | Accuracy | ROC AUC | F1   | Inference Time | Model Size |
|------------|----------|---------|------|----------------|------------|
| baseline   | 0.65     | 0.70    | 0.62 | 1ms           | 10KB       |
| xgboost    | 0.87     | 0.92    | 0.85 | 35ms          | 12MB       |
| lightgbm   | 0.86     | 0.91    | 0.84 | 28ms          | 8MB        |
| neural-net | 0.85     | 0.90    | 0.83 | 120ms         | 45MB       |

Recommendation: XGBoost
- Best accuracy and AUC
- Acceptable inference time (<50ms requirement)
- Good size/performance tradeoff

Best Practices

1. Always compare to baseline - Random, majority, rule-based
2. Use cross-validation - Never trust single split
3. Check calibration - Are probabilities meaningful?
4. Analyze errors - What types of mistakes?
5. Test statistical significance - Is improvement real?

Integration with SpecWeave

# Evaluate model in increment
/ml:evaluate-model 0042

# Compare all models in increment
/ml:compare-models 0042

# Generate full evaluation report
/ml:evaluation-report 0042

Evaluation results automatically included in increment COMPLETION-SUMMARY.md.