Yellowbrick is a suite of visual analysis and diagnostic tools designed to facilitate machine learning with Scikit-Learn. The package includes visualizations that can help users navigate the feature selection process, build intuition around model selection, diagnose common problems like bias, heteroscedasticity, underfit, and overtraining, and support hyperparameter tuning to steer predictive models toward more successful results.
Some of the available tools include:
histograms
scatter plot matrices
parallel coordinates
jointplots
ROC curves
classification heatmaps
residual plots
validation curves
gridsearch heatmaps
For more, please see the full documentation at: http://yellowbrick.readthedocs.org/en/latest/
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3 maintainers
Release files for yellowbrick 0.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| yellowbrick-0.3.1.tar.gz | 3.9 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| yellowbrick-0.3.1-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details |
Total release size: 3.9 MB
Release files / yellowbrick-0.3.1.tar.gz
| Download URL | yellowbrick-0.3.1.tar.gz |
|---|---|
| Size | 3.9 MB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / yellowbrick-0.3.1-py2.py3-none-any.whl
| Download URL | yellowbrick-0.3.1-py2.py3-none-any.whl |
|---|---|
| Size | 46.5 kB |
| Tags | Python 2 Python 3 |
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SHA-256 checksum How to use checksums |
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