Evaluate ML Model

You are evaluating an ML model in a SpecWeave increment. Generate a comprehensive evaluation report following ML best practices.

Your Task

1. Load Model: Load the model from the specified increment
2. Run Evaluation: Execute comprehensive evaluation with appropriate metrics
3. Generate Report: Create evaluation report in increment folder

Evaluation Steps

Step 1: Identify Model Type

• Classification: accuracy, precision, recall, F1, ROC AUC, confusion matrix
• Regression: RMSE, MAE, MAPE, R², residual analysis
• Ranking: precision@K, recall@K, NDCG@K, MAP

Step 2: Load Test Data

# Load test set from increment
X_test = load_test_data(increment_path)
y_test = load_test_labels(increment_path)

Step 3: Compute Metrics

from specweave import ModelEvaluator

evaluator = ModelEvaluator(model, X_test, y_test)
metrics = evaluator.compute_all_metrics()

Step 4: Generate Visualizations

• Confusion matrix (classification)
• ROC curves (classification)
• Residual plots (regression)
• Calibration curves (classification)

Step 5: Statistical Validation

• Cross-validation results
• Confidence intervals
• Comparison to baseline
• Statistical significance tests

Step 6: Generate Report

Create evaluation-report.md in increment folder:

# Model Evaluation Report

## Model: [Model Name]
- Version: [Version]
- Increment: [Increment ID]
- Date: [Evaluation Date]

## Overall Performance
[Metrics table]

## Visualizations
[Embedded plots]

## Cross-Validation
[CV results]

## Comparison to Baseline
[Baseline comparison]

## Statistical Tests
[Significance tests]

## Recommendations
[Deploy/improve/investigate]

Output

After evaluation, report:

• Overall performance summary
• Key metrics
• Whether model meets success criteria (from spec.md)
• Recommendation (deploy/improve/investigate)