Evaluate Model

Measure machine learning model performance using appropriate metrics for the task (classification, regression, etc.).

When to Use

• Comparing different model architectures
• Assessing performance on test/validation datasets
• Detecting overfitting or underfitting
• Reporting model accuracy for papers and documentation

Quick Reference

# Mojo model evaluation pattern
struct ModelEvaluator:
    fn evaluate_classification(
        mut self,
        predictions: ExTensor,
        ground_truth: ExTensor
    ) -> Tuple[Float32, Float32, Float32]:
        # Returns accuracy, precision, recall
        ...

    fn evaluate_regression(
        mut self,
        predictions: ExTensor,
        ground_truth: ExTensor
    ) -> Tuple[Float32, Float32]:
        # Returns MSE, MAE
        ...

Workflow

1. Load test data: Prepare test/validation dataset
2. Generate predictions: Run model inference on test set
3. Select metrics: Choose appropriate metrics (accuracy, precision, recall, F1, AUC, MSE, etc.)
4. Calculate metrics: Compute performance metrics
5. Analyze results: Compare to baseline and identify strengths/weaknesses

Output Format

Evaluation report:

• Task type (classification, regression, etc.)
• Metrics (accuracy, precision, recall, F1, AUC, etc.)
• Per-class breakdown (if applicable)
• Comparison to baseline model
• Confusion matrix (classification)
• Error analysis

References

• See CLAUDE.md > Language Preference (Mojo for ML models)
• See

  train-model

  skill for model training
• See

  /notes/review/mojo-ml-patterns.md

  for Mojo tensor operations