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Visual analysis and diagnostic tools to facilitate machine learning model selection.
| Date | Stars |
|---|---|
| 2026-07-31 | 4399 |
| 2026-08-02 | 4399 |
| 2026-08-06 | 4399 |
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# Yellowbrick
[](https://github.com/DistrictDataLabs/yellowbrick/actions/workflows/ci.yml)
[](https://codecov.io/gh/DistrictDataLabs/yellowbrick)
[](https://lgtm.com/projects/g/DistrictDataLabs/yellowbrick/alerts/)
[](https://lgtm.com/projects/g/DistrictDataLabs/yellowbrick/context:python)
[](https://badge.fury.io/py/yellowbrick)
[](http://yellowbrick.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/psf/black)
[](https://doi.org/10.5281/zenodo.1206239)
[](https://doi.org/10.21105/joss.01075)
[](https://mybinder.org/v2/gh/DistrictDataLabs/yellowbrick/develop?filepath=examples%2Fexamples.ipynb)
**Visual analysis and diagnostic tools to facilitate machine learning model selection.**
[](https://www.scikit-yb.org/en/latest/gallery.html)
## What is Yellowbrick?
Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for _your_ machine learning workflow!
For complete documentation on the Yellowbrick API, a gallery of available visualizers, the contributor's guide, tutorials and teaching resources, frequently asked questions, and more, please visit our documentation at [www.scikit-yb.org](https://www.scikit-yb.org/).
## Installing Yellowbrick
Yellowbrick is compatible with Python 3.4 or later and also depends on scikit-learn and matplotlib. The simplest way to install Yellowbrick and its dependencies is from PyPI with pip, Python's preferred package installer.
$ pip install yellowbrick
Note that Yellowbrick is an active project and routinely publishes new releases with more visualizers and updates. In order to upgrade Yellowbrick to the latest version, use pip as follows.
$ pip install -U yellowbrick
You can also use the `-U` flag to update scikit-learn, matplotlib, or any other third party utilities that work well with Yellowbrick to their latest versions.
If you're using Anaconda (recommended for Windows users), you can take advantage of the conda utility to install Yellowbrick:
conda install -c districtdatalabs yellowbrick
## Using Yellowbrick
The Yellowbrick API is specifically designed to play nicely with scikit-learn. Here is an example of a typical workflow sequence with scikit-learn and Yellowbrick:
### Feature Visualization
In this example, we see how Rank2D performs pairwise comparisons of each feature in the data set with a specific metric or algorithm and then returns them ranked as a lower left triangle diagram.
```python
from yellowbrick.features import Rank2D
visualizer = Rank2D(
features=features, algorithm='covariance'
)
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.transform(X) # Transform the data
visualizer.show() # Finalize and render the figure
```
### Model Visualization
In this example, we instantiate a scikit-learn classifier and then use Yellowbrick's ROCAUC class toExcerpt of 8,545 characters
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matched fp:b1b84db733717307, llm:Repository topics and description: 'machine-learning', 'visualization', 'visual-analysis', 'visualizer'; README: 'Visual analysis and diagnostic tools to facilitate machine learning model selection', 'extends the scikit-learn API', 'visual diagnostic tools called Visualizers'.
matched fp:b1b84db733717307, llm:Repository topics and description: 'machine-learning', 'visualization', 'visual-analysis', 'visualizer'; README: 'Visual analysis and diagnostic tools to facilitate machine learning model selection', 'extends the scikit-learn API', 'visual diagnostic tools called Visualizers'.
matched fp:b1b84db733717307, llm:Repository topics and description: 'machine-learning', 'visualization', 'visual-analysis', 'visualizer'; README: 'Visual analysis and diagnostic tools to facilitate machine learning model selection', 'extends the scikit-learn API', 'visual diagnostic tools called Visualizers'.