ML project structure in ML Python - Model Metrics & Evaluation

Metrics & Evaluation - ML project structure

Which metric matters for ML project structure and WHY

In ML project structure, the key metrics are related to model performance and workflow efficiency. Metrics like accuracy, precision, recall, and F1 score help us know if the model works well. Metrics on code quality, test coverage, and runtime show if the project is easy to maintain and run. Good structure helps track and improve these metrics easily.

Confusion matrix or equivalent visualization

While ML project structure itself does not have a confusion matrix, the models inside the project do. For example, a confusion matrix looks like this:

      | Predicted Positive | Predicted Negative |
      |--------------------|--------------------|
      | True Positive (TP)  | False Positive (FP) |
      | False Negative (FN) | True Negative (TN)  |
    

This matrix helps measure precision, recall, and accuracy, which are key to evaluating model quality within the project.

Precision vs Recall tradeoff with concrete examples

In an ML project, choosing which metric to focus on depends on the problem. For example:

• Spam filter: High precision is important to avoid marking good emails as spam.
• Medical diagnosis: High recall is critical to catch all sick patients, even if some healthy ones are flagged.

A well-structured project makes it easy to test and compare these metrics to find the best balance.

What "good" vs "bad" metric values look like for ML project structure

Good:

• Model metrics: Accuracy, precision, recall, and F1 score are high and balanced.
• Project metrics: Clear folder organization, modular code, automated tests, and reproducible results.

Bad:

• Model metrics: High accuracy but low recall or precision, indicating poor real-world use.
• Project metrics: Messy code, no tests, hard to reproduce results, and unclear data flow.

Metrics pitfalls in ML project structure

• Accuracy paradox: High accuracy can be misleading if data is imbalanced.
• Data leakage: When test data leaks into training, metrics look better but model fails in real use.
• Overfitting indicators: Training metrics are great but test metrics are poor, showing the model memorizes instead of learning.
• Poor project structure: Makes it hard to track metrics, reproduce results, or improve models.

Self-check question

Your model has 98% accuracy but only 12% recall on fraud cases. Is it good for production? Why or why not?

Answer: No, it is not good. The model misses most fraud cases (low recall), which is dangerous. High accuracy is misleading because fraud is rare. You need to improve recall to catch more fraud.

Key Result

Good ML project structure supports clear tracking and improvement of key model metrics like precision, recall, and accuracy.