Yellowbrick v1.2

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.

Major Changes:

• Added Q-Q plot as side-by-side option to the ResidualsPlot visualizer.

• More robust handling of binary classification in ROCAUC visualization, standardizing the way that classifiers with predict_proba and decision_function methods are handling. A binary hyperparameter was added to the visualizer to ensure correct interpretation of binary ROCAUC plots.

• Fixes to ManualAlphaSelection to move it from prototype to prime time including documentation, tests, and quick method. This method allows users to perform alpha selection visualization on non-CV estimators.

• Removal of AppVeyor from the CI matrix after too many out-of-core (non-Yellowbrick) failures with setup and installation on the VisualStudio images. Yellowbrick CI currently omits Windows and Miniconda from the test matrix and we are actively looking for new solutions.

• Third party estimator wrapper in contrib to provide enhanced support for non-scikit-learn estimators such as those in Keras, CatBoost, and cuML.

Minor Changes:

• Allow users to specify colors for the PrecisionRecallCurve.

• Update ClassificationScoreVisualizer base class to have a class_colors_ learned attribute instead of a colors property; additional polishing of multi-class colors in PrecisionRecallCurve, ROCAUC, and ClassPredictionError.

• Update KElbowVisualizer fit method and quick method to allow passing sample_weight parameter through the visualizer.

• Enhancements to classification documentation to better discuss precision and recall and to diagnose with PrecisionRecallCurve and ClassificationReport visualizers.

• Improvements to CooksDistance visualizer documentation.

• Corrected KElbowVisualizer label and legend formatting.

• Typo fixes to ROCAUC documentation, labels, and legend. Typo fix to Manifold documentation.

• Use of tight_layout accessing the Visualizer figure property to finalize images and resolve discrepancies in plot directive images in documentation.

• Add get_param_names helper function to identify keyword-only parameters that belong to a specific method.

• Splits package namespace for yellowbrick.regressor.residuals to move PredictionError to its own module, yellowbrick.regressor.prediction_error.

• Update tests to use SVC instead of LinearSVC and correct KMeans scores based on updates to scikit-learn v0.23.

• Continued maintenance and management of baseline images following dependency updates; removal of mpl.cbook dependency.

• Explicitly include license file in source distribution via MANIFEST.in.

• Fixes to some deprecation warnings from sklearn.metrics.

• Testing requirements depends on Pandas v1.0.4 or later.

• Reintegrates pytest-spec and verbose test logging, updates pytest dependency to v0.5.0 or later.

• Added Pandas v0.20 or later to documentation dependencies.