A suite of visual analysis and diagnostic tools for machine learning.
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:
- scatter plot matrices
- parallel coordinates
- 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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|File Name & Checksum SHA256 Checksum Help||Version||File Type||Upload Date|
|yellowbrick-0.1-py2-none-any.whl (7.8 kB) Copy SHA256 Checksum SHA256||py2||Wheel||May 18, 2016|
|yellowbrick-0.1.tar.gz (5.6 kB) Copy SHA256 Checksum SHA256||–||Source||May 18, 2016|