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https://github.com/probabl-ai/skore
the scikit-learn sidekick
https://github.com/probabl-ai/skore
data-analysis data-science data-visualization machine-learning python scikit-learn workflow
Last synced: 2 days ago
JSON representation
the scikit-learn sidekick
- Host: GitHub
- URL: https://github.com/probabl-ai/skore
- Owner: probabl-ai
- License: mit
- Created: 2024-06-17T15:29:38.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-01-31T16:36:20.000Z (8 days ago)
- Last Synced: 2025-01-31T16:40:48.308Z (8 days ago)
- Topics: data-analysis, data-science, data-visualization, machine-learning, python, scikit-learn, workflow
- Language: Python
- Homepage: https://skore.probabl.ai
- Size: 10 MB
- Stars: 275
- Watchers: 6
- Forks: 19
- Open Issues: 83
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
README
[](https://pepy.tech/projects/skore)
[](https://pypi.org/project/skore/)
[](https://discord.probabl.ai/)
the scikit-learn sidekick
Elevate ML Development with Built-in Recommended Practices \
[Documentation](https://skore.probabl.ai) — [Community](https://discord.probabl.ai)
## What is skore?
skore is a Python open-source library designed to help data scientists apply recommended practices and avoid common methodological pitfalls in scikit-learn.
## Key features
- **Diagnose**: catch methodological errors before they impact your models.
- `train_test_split` supercharged with methodological guidance: the API is the same as scikit-learn's, but skore displays warnings when applicable. For example, it warns you against shuffling time series data or when you have class imbalance.
- **Evaluate**: automated insightful reports.
- `EstimatorReport`: feed your scikit-learn compatible estimator and dataset, and it generates recommended metrics and plots to help you analyze your estimator. All these are computed and generated for you in 1 line of code. Under the hood, we use efficient caching to make the computations blazing fast.
- `CrossValidationReport`: Get a skore estimator report for each fold of your cross-validation.
## What's next?
Skore is just at the beginning of its journey, but we’re shipping fast! Frequent updates and new features are on the way as we work toward our vision of becoming a comprehensive library for data scientists.
⭐ Support us with a star and spread the word - it means a lot! ⭐
## 🚀 Quick start
### Installation
#### With pip
We recommend using a [virtual environment (venv)](https://docs.python.org/3/tutorial/venv.html). You need `python>=3.9`.
Then, you can install skore by using `pip`:
```bash
pip install -U skore
```
#### With conda
skore is available in `conda-forge`:
```bash
conda install conda-forge::skore
```
You can find information on the latest version [here](https://anaconda.org/conda-forge/skore).
### Get assistance when developing your ML/DS projects
1. Evaluate your model using `skore.CrossValidationReport`:
```python
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from skore import CrossValidationReport
X, y = make_classification(n_classes=2, n_samples=100_000, n_informative=4)
clf = LogisticRegression()
cv_report = CrossValidationReport(clf, X, y)
# Display the help tree to see all the insights that are available to you
cv_report.help()
```
```python
# Display the report metrics that was computed for you:
df_cv_report_metrics = cv_report.metrics.report_metrics()
df_cv_report_metrics
```
```python
# Display the ROC curve that was generated for you:
roc_plot = cv_report.metrics.plot.roc()
roc_plot
```
1. Store your results for safe-keeping.
```python
# Create and load a skore project
import skore
my_project = skore.Project("my_project")
```
```python
# Store your results
my_project.put("df_cv_report_metrics", df_cv_report_metrics)
my_project.put("roc_plot", roc_plot)
```
```python
# Get your results
df_get = my_project.put("df_cv_report_metrics")
```
Learn more in our [documentation](https://skore.probabl.ai).
## Contributing
Thank you for considering contributing to skore! Join our mission to promote open-source and make machine learning development more robust and effective. Please check the contributing guidelines [here](https://github.com/probabl-ai/skore/blob/main/CONTRIBUTING.rst).
## Feedback & Community
- Join our [Discord](https://discord.probabl.ai/) to share ideas or get support.
- Request a feature or report a bug via [GitHub Issues](https://github.com/probabl-ai/skore/issues).
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