This repository is here to help you test and provide feedback on skore, a new Python library that aims to enhance scikit-learn for data scientists. skore is currently a work-in-progress, and more features are to be expected.
Skore is developed by the product team at Probabl, a scikit-learn company. For more info, check us out: Notion page.
In total, this test drive should last under an hour.
- Presentation of Probabl and skore: ~5 min
- Environment setup: ~5 min
- User testing tasks: ~35 min
- Here is a notebook to get you started:
compare_models.ipynb.
- Here is a notebook to get you started:
- Feedback Q&A: ~15 min
- Clone this GitHub repository locally.
- Create a Python virtual environment with your usual method (conda, virtualenv...), with
python>=3.9. - Install the libraries in
requirements.txtwith (activate your venv beforehand):pip install -r requirements.txt
The goal is to build a couple of models, and to compare them, using skore when relevant.
Resources:
- Notebook:
compare_models.ipynb - Skore documentation
Let us know what you think of skore! Here are some topics on which we would appreciate your feedback:
- Overall experience
- First impressions?
- Pain points?
- Unexpected discoveries?
- Analysis flow
- How natural was the storage/retrieval?
- Dashboard creation experience?
- What would improve your workflow?
- Suggestions
- Missing features?
- Onboarding improvements?
- Would you use skore in your day-to-day work? Would you recommend skore to a colleague?
You can share your feedback:
- either directly by mail to the Product Team,
- or on the
#skorechannel of our Discord server.
- Give a star to the skore repository
- Tell your friends, invite them to test skore with this repo (https://github.com/probabl-ai/skore-user-test)
- Bring discussions to our Discord server