Yellowbrick is a suite of visual analysis and diagnostic tools designed to facilitate machine learning with Scikit-Learn. The library implements a new core API object, the “Visualizer” that is an Scikit-Learn estimator: an object that learns from data. Like transformers or models, visualizers learn from data by creating a visual representation of the model selection workflow.
Visualizers allow users to steer the model selection process, building intuition around feature engineering, algorithm selection, and hyperparameter tuning. For example, visualizers can help diagnose common problems surrounding model complexity and bias, heteroscedasticity, underfit and overtraining, or class balance issues. By applying visualizers to the model selection workflow, Yellowbrick allows you to steer predictive models to more successful results, faster.
Some of the available tools include:
pairwise feature ranking
parallel coordinates
radial visualization
ROC curves
classification heatmaps
residual plots
prediction error plots
alpha selection plots
validation curves
gridsearch heatmaps
text frequency distributions
tsne corpus visualization
And much more! Please see the full documentation at: http://scikit-yb.org/
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Release files for yellowbrick 0.4
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Source distribution (sdist)
| File | Size | Uploaded | |
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| yellowbrick-0.4.tar.gz | 16.0 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| yellowbrick-0.4-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details |
Total release size: 16.1 MB
Release files / yellowbrick-0.4.tar.gz
| Download URL | yellowbrick-0.4.tar.gz |
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| Size | 16.0 MB |
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Release files / yellowbrick-0.4-py2.py3-none-any.whl
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|---|---|
| Size | 109.0 kB |
| Tags | Python 2 Python 3 |
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