guap

From algorithms outputs to business outcomes.

guap is an open-source python package that helps data team to get an ML evaluation metrics everyone can agree on by converting your model output to business outcomes, a.k.a. profits.

🤖 🪄 📈

🧞‍♂️ Why should I use guap?

Our mission with guap is to align all stakeholders with measurable business outcomes by including the three core teams — business, data science and IT — throughout the life cycle of the AI models.

• Make collaboration healthier and clearer between tech and non-tech people
• Make better decisions at every stage of the ML project lifecycle

Want to know more? Read Why guap exist.

✨ Features

We're on the journey to make sure every ML use-case that go to production is a valuable one. And it starts with a simple way to estimate the expected profit/cost of a model based on its confusion matrix.

• Get the total profit Based on the test set, guap will give you the total expected profit based on the cost matrix. A great way to have an overview of the model profitability.
• Average profit per prediction Along the total profit score, guap will give you the average profit/cost per prediction. Perfect if you have costs per prediction, or if you need to estimate the profitability while scaling.

That's it...for now! Keep up-to-date with release announcements on Twitter @guap_ml!

🪄 Quickstart Install

First install the package using pip

pip install guap

Then, you can follow our instructions using the Google Colab demo. We're writing the documentation right now.

⌛ Status

• Alpha: We are demoing guap to users and receiving feedback
• Private Beta
• Public Beta
• Official Launch

Watch "releases" of this repo to get notified of major updates, and give the star button a click whilst you're there.

🙏 Contributing

Pull requests are welcome. You don't know where to start? let's talk @guap_ml!

💖 License

Apache License 2.0