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 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.
Please see the full documentation at: http://scikit-yb.org/
Visualizers are estimators (objects that learn from data) whose primary objective is to create visualizations that allow insight into the model selection process. In Scikit-Learn terms, they can be similar to transformers when visualizing the data space or wrap an model estimator similar to how the “ModelCV” (e.g. RidgeCV, LassoCV) methods work. The primary goal of Yellowbrick is to create a sensical API similar to Scikit-Learn. Some of our most popular visualizers include:
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|yellowbrick-0.4.2-py2.py3-none-any.whl (123.0 kB) Copy SHA256 Checksum SHA256||py2.py3||Wheel||May 22, 2017|
|yellowbrick-0.4.2.tar.gz (17.7 MB) Copy SHA256 Checksum SHA256||–||Source||May 22, 2017|