Yellowbrick v1.0

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.

Note: Python 2 Deprecation: Please note that this release deprecates Yellowbrick's support for Python 2.7. After careful consideration and following the lead of our primary dependencies (NumPy, scikit-learn, and Matplolib), we have chosen to move forward with the community and support Python 3.4 and later.

Major Changes:

• New JointPlot visualizer that is specifically designed for machine learning. The new visualizer can compare a feature to a target, features to features, and even feature to feature to target using color. The visualizer gives correlation information at a glance and is designed to work on ML datasets.
• New PosTagVisualizer is specifically designed for diagnostics around natural language processing and grammar-based feature extraction for machine learning. This new visualizer shows counts of different parts-of-speech throughout a tagged corpus.
• New datasets module that provide greater support for interacting with Yellowbrick example datasets including support for Pandas, npz, and text corpora.
• Management repository for Yellowbrick example data, yellowbrick-datasets.
• Add support for matplotlib 3.0.1 or greater.
• UMAPVisualizer as an alternative manifold to TSNE for corpus visualization that is fast enough to not require preprocessing PCA or SVD decomposition and preserves higher order similarities and distances.
• Added ..plot:: directives to the documentation to automatically build the images along with the docs and keep them as up to date as possible. The directives also include the source code making it much simpler to recreate examples.
• Added target_color_type functionality to determine continuous or discrete color representations based on the type of the target variable.
• Added alpha param for both test and train residual points in ResidualsPlot.
• Added frameon param to Manifold.
• Added frequency sort feature to PosTagVisualizer.
• Added elbow detection using the "kneedle" method to the KElbowVisualizer.
• Added governance document outlining new Yellowbrick structure.
• Added CooksDistance regression visualizer.
• Updated DataVisualizer to handle target type identification.
• Extended DataVisualizer and updated its subclasses.
• Added ProjectionVisualizer base class.
• Restructured yellowbrick.target, yellowbrick.features, and yellowbrick.model_selection API.
• Restructured regressor and classifier API.

Minor Changes:

• Updated Rank2D to include Kendall-Tau metric.
• Added user specification of ISO F1 values to PrecisionRecallCurve and updated the quick method to accept train and test splits.
• Added code review checklist and conventions to the documentation and expanded the contributing docs to include other tricks and tips.
• Added polish to missing value visualizers code, tests, and documentation.
• Improved RankD tests for better coverage.
• Added quick method test for DispersionPlot visualizer.
• BugFix: fixed resolve colors bug in TSNE and UMAP text visualizers and added regression tests to prevent future errors.
• BugFix: Added support for Yellowbrick palettes to return colormap.
• BugFix: fixed PrecisionRecallCurve visual display problem with multi-class labels.
• BugFix: fixed the RFECV step display bug.
• BugFix: fixed error in distortion score calculation.
• Extended FeatureImportances documentation and tests for stacked importances and added a warning when stack should be true.
• Improved the documentation readability and structure.
• Refreshed the README.md and added testing and documentation READMEs.
• Updated the gallery to generate thumbnail-quality images.
• Updated the example notebooks and created a quickstart notebook.
• Fixed broken links in the documentation.
• Enhanced the SilhouetteVisualizer with legend and color parameter, while also move labels to the y-axis.
• Extended FeatureImportances docs/tests for stacked importances.
• Documented the yellowbrick.download script.
• Added JOSS citation for "Yellowbrick: Visualizing the Scikit-Learn Model Selection Process".
• Added new pull request (PR) template.
• Added alpha param to PCA Decomposition Visualizer.
• Updated documentation with affiliations.
• Added a windows_tol for the visual unittest suite.
• Added stacked barchart to PosTagVisualizer.
• Let users set colors for FreqDistVisualizer and other ax_bar visualizers.
• Updated Manifold to extend ProjectionVisualizer.
• Check if an estimator is already fitted before calling fit method.
• Ensure poof returns ax.

Compatibility Notes:

• This version provides support for matplotlib 3.0.1 or greater and drops support for matplotlib versions less than 2.0.
• This version drops support for Python 2