Yellowbrick v0.5

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 

• Added VisualTestCase.
• New PCADecomposition Visualizer, which decomposes high dimensional data into two or three dimensions so that each instance can be plotted in a scatter plot.
• New and improved ROCAUC Visualizer, which now supports multiclass classification.
• Prototype Decision Boundary Visualizer, which is a bivariate data visualization algorithm that plots the decision boundaries of each class.
• Added Rank1D Visualizer, which is a one dimensional ranking of features that utilizes the Shapiro-Wilks ranking that takes into account only a single feature at a time (e.g. histogram analysis).
• Improved Prediction Error Plot with identity line, shared limits, and r squared.
• Updated FreqDist Visualizer to make word features a hyperparameter.
• Added normalization and scaling to Parallel Coordinates.
• Added Learning Curve Visualizer, which displays a learning curve based on the number of samples versus the training and cross validation scores to show how a model learns and improves with experience.
• Added data downloader module to the yellowbrick library.
• Complete overhaul of the yellowbrick documentation; categories of methods are located in separate pages to make it easier to read and contribute to the documentation.
• Added a new color palette inspired by ANN-generated colors

Bug Fixes:

• Repairs to PCA, RadViz, FreqDist unit tests
• Repair to matplotlib version check in JointPlot Visualizer