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

Ujayer Hasnat — Sat, 04 Jul 2026 07:43:22 +0000

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

Most machine learning workflows end with a few familiar metrics:

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:

That inspired me to build 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?"

import modeldoctor as md

report = md.diagnose(model)

report.show()

ModelDoctor automatically evaluates:

and provides actionable recommendations backed by diagnostic evidence.

Some highlights:

I wanted a tool that could answer questions like:

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

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!