Metadata-Version: 2.0
Name: yellowbrick
Version: 0.4
Summary: A suite of visual analysis and diagnostic tools for machine learning.
Home-page: https://github.com/districtdatalabs/yellowbrick
Author: Rebecca Bilbro
Author-email: rbilbro@districtdatalabs.com
License: Apache 2
Download-URL: https://github.com/districtdatalabs/yellowbrick/tarball/v0.4
Keywords: visualization,machine learning,scikit-learn,matplotlib,data science
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Other Environment
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Dist: cycler (>=0.10.0)
Requires-Dist: matplotlib (>=1.5.1)
Requires-Dist: numpy (>=1.11.0)
Requires-Dist: scikit-learn (>=0.17.1)
Requires-Dist: scipy (>=0.17.1)

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/


