Model Evaluation

Overview

This section describes our model evaluation process. We use DVC to track evaluation metrics and compare different model versions.

Evaluation Pipeline

Our evaluation process includes:

1. Metric Collection
2. Accuracy
3. Precision
4. Recall
5. F1 Score

6. Custom metrics

7. Model Comparison

8. Compare different models
9. Compare model versions
10. Track improvements
11. Visualize results

Pipeline Configuration

The evaluation pipeline is defined in dvc.yaml:

stages:
  evaluate_model:
    cmd: python src/mlops/evaluation/evaluate.py
    deps:
      - data/processed/test.csv
      - models/trained/model.pkl
      - src/mlops/evaluation/evaluate.py
    metrics:
      - metrics/evaluation.json:
          cache: false

Usage

To evaluate models:

# Run evaluation pipeline
dvc repro evaluate_model

# Show metrics
dvc metrics show

# Compare with previous version
dvc metrics diff

# Show detailed comparison
dvc exp show

Model Comparison

Example of comparing different models:

{
  "random_forest": {
    "accuracy": 0.85,
    "precision": 0.83,
    "recall": 0.86,
    "f1": 0.84
  },
  "gradient_boost": {
    "accuracy": 0.87,
    "precision": 0.86,
    "recall": 0.85,
    "f1": 0.85
  }
}

Best Practices

1. Metric Selection
2. Choose relevant metrics
3. Consider business impact

4. Track multiple metrics

5. Evaluation Process

6. Use consistent test data
7. Document evaluation criteria

8. Version evaluation code

9. Results Documentation

10. Record all experiments
11. Document improvements
12. Maintain comparison logs