https://github.com/edublancas/sklearn-evaluation

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.
https://github.com/edublancas/sklearn-evaluation

Last synced: 19 days ago
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Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

• Host: GitHub
• URL: https://github.com/edublancas/sklearn-evaluation
• Owner: edublancas
• License: mit
• Fork: true (ploomber/sklearn-evaluation)
• Created: 2023-01-15T21:18:52.000Z (about 2 years ago)
• Default Branch: master
• Last Pushed: 2023-01-15T22:50:39.000Z (about 2 years ago)
• Last Synced: 2025-03-24T04:17:28.291Z (22 days ago)
• Homepage: https://sklearn-evaluation.ploomber.io
• Size: 12.9 MB
• Stars: 3
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-datascience - sklearn-evaluation

README

        
# sklearn-evaluation

![CI](https://github.com/ploomber/sklearn-evaluation/workflows/CI/badge.svg)

[![Documentation Status](https://readthedocs.org/projects/sklearn-evaluation/badge/?version=latest)](https://sklearn-evaluation.readthedocs.io/en/latest/?badge=latest)

[![PyPI version](https://badge.fury.io/py/sklearn-evaluation.svg)](https://badge.fury.io/py/sklearn-evaluation)

[![Coverage Status](https://coveralls.io/repos/github/ploomber/sklearn-evaluation/badge.svg)](https://coveralls.io/github/ploomber/sklearn-evaluation)

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[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)



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Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking, and Jupyter notebook analysis.

Supports Python 3.7 and higher. Tested on Linux, macOS and Windows.

*Note:* Recent versions likely work on Python 3.6, however, `0.8.2` was the latest version we tested with Python 3.6.



    



![confusion matrix](examples/cm.png)

# Install  

```bash

pip install sklearn-evaluation

```

# Features

* [Plotting](https://sklearn-evaluation.ploomber.io/en/latest/classification/basic.html) (confusion matrix, feature importances, precision-recall, roc, elbow curve, silhouette plot)

* Report generation ([example](https://htmlpreview.github.io/?https://github.com/ploomber/sklearn-evaluation/blob/master/examples/report.html))

* [Evaluate grid search results](https://sklearn-evaluation.ploomber.io/en/latest/optimization/grid_search.html)

* [Track experiments using a local SQLite database](https://sklearn-evaluation.ploomber.io/en/latest/comparison/SQLiteTracker.html)

* [Analyze notebooks output](https://sklearn-evaluation.ploomber.io/en/latest/comparison/NotebookCollection.html)

* [Query notebooks with SQL](https://sklearn-evaluation.ploomber.io/en/latest/comparison/nbdb.html)