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

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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)

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

[![Downloads](https://static.pepy.tech/badge/sklearn-evaluation)](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)



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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 tested with such Python version.



    



![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/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)