# 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 we tested with Python 3.6.

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![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)