I Built a Python Library That Diagnoses Machine Learning Models Before Deployment

I Built ModelDoctor — A Python Library That Diagnoses Machine Learning Models

Most machine learning workflows end with a few familiar metrics:

• Accuracy
• F1 Score
• Precision
• Recall
• ROC AUC

But after working on several ML projects, I realized these numbers don't always tell the full story.

A model can achieve 98% accuracy and still have serious problems like:

• Overfitting
• Data leakage
• Poor probability calibration
• Weak generalization
• Production bottlenecks

That inspired me to build ModelDoctor.

What is ModelDoctor?

ModelDoctor is an open-source Python library that analyzes trained machine learning models and generates an evidence-based health report.

Instead of only answering:

"How accurate is my model?"

It helps answer:

"Can I trust this model in production?"

Example

import modeldoctor as md

report = md.diagnose(model)

report.show()

ModelDoctor automatically evaluates:

• Overfitting
• Data leakage
• Calibration
• Feature quality
• Generalization
• Prediction quality
• Production readiness

and provides actionable recommendations backed by diagnostic evidence.

Built for Developers

Some highlights:

• One-line API
• Interactive HTML reports
• JSON & PDF export
• Validation framework with 54 benchmark scenarios
• MIT Licensed
• Fully open source

Why I Built It

I wanted a tool that could answer questions like:

• Is my model actually overfitting?
• Are my features leaking information?
• Can I trust the predicted probabilities?
• Is this model ready for deployment?

Instead of manually checking each of these, ModelDoctor brings everything together into a single diagnostic report.

Try It

pip install modeldoctor

GitHub:

https://github.com/CodexUjayer/Model-Doctor

I'd love to hear your feedback, feature ideas, or suggestions for additional diagnostics. Contributions are always welcome!