Yellowbrick v0.3.2

Yellowbrick is an open source, pure Python project that extends the scikit-learn API with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib to create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models and assist in diagnosing problems throughout the machine learning workflow. Changes Hardened the Yellowbrick API to elevate the idea of a Visualizer to a first principle. This included reconciling shifts in the development of the preliminary versions to the new API, formalizing Visualizer methods like draw() and finalize(), and adding utilities that revolve around Scikit-Learn. To that end we also performed administrative tasks like refreshing the documentation and preparing the repository for more and varied open source contributions. Converted Mkdocs documentation to Sphinx documentation Updated docstrings for all Visualizers and functions Created a DataVisualizer base class for dataset visualization Single call functions for simple visualizer interaction Added yellowbrick specific color sequences and palettes and env handling More robust examples with downloader from DDL host Better axes handling in visualizer, matplotlib/sklearn integration Added a finalize method to complete drawing before render Improved testing on real data sets from examples Bugfixes Score visualizer renders in notebook but not in Python scripts. Tests updated to support new API Hotfix 0.3.1 Hotfix to solve pip install issues with Yellowbrick. Modified packaging and wheel for Python 2.7 and 3.5 compatibility Modified deployment to PyPI and pip install ability Fixed Travis-CI tests with the backend failures