Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ploomber/sklearn-evaluation
Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.
https://github.com/ploomber/sklearn-evaluation
data-science deep-learning jupyter-notebook machine-learning pytorch scikit-learn sklearn tensorflow
Last synced: 6 days ago
JSON representation
Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.
- Host: GitHub
- URL: https://github.com/ploomber/sklearn-evaluation
- Owner: ploomber
- License: apache-2.0
- Created: 2015-09-04T16:33:42.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2024-09-18T16:57:14.000Z (5 months ago)
- Last Synced: 2024-10-29T17:11:51.684Z (4 months ago)
- Topics: data-science, deep-learning, jupyter-notebook, machine-learning, pytorch, scikit-learn, sklearn, tensorflow
- Language: Python
- Homepage: https://sklearn-evaluation.ploomber.io
- Size: 20.1 MB
- Stars: 456
- Watchers: 16
- Forks: 54
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-python-machine-learning-resources - GitHub - 20% open · ⏱️ 22.08.2022): (模型的可解释性)
README
# sklearn-evaluation

[](https://sklearn-evaluation.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/sklearn-evaluation)
[](https://coveralls.io/github/ploomber/sklearn-evaluation)
[](https://twitter.com/intent/user?screen_name=ploomber)
[](https://github.com/psf/black)
[](https://pepy.tech/project/sklearn-evaluation)> [!TIP]
> Deploy AI apps for free on [Ploomber Cloud!](https://ploomber.io/?utm_medium=github&utm_source=sklearn-evaluation)
Join our community
|
Newsletter
|
Contact us
|
Docs
|
Blog
|
Website
|
YouTubeMachine 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 tested with such Python version.

# 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/classification/optimization.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)