Deployed: Friday, October 9, 2020
Current Contributors: @rebeccabilbro, @lwgray, @VladSkripniuk, @Express50, @pdamodaran, @aldermartinez, @tktran, @bbengfort, @melonhead901, @Kautumn06, @ojedatony1616, @eschmier, @wagner2010, @ndanielsen
- Added Q-Q plot as side-by-side option to the
- More robust handling of binary classification in
ROCAUCvisualization, standardizing the way that classifiers with
decision_functionmethods are handling. A
binaryhyperparameter was added to the visualizer to ensure correct interpretation of binary ROCAUC plots.
- Fixes to
ManualAlphaSelectionto 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.
- Allow users to specify colors for the
ClassificationScoreVisualizerbase class to have a
class_colors_learned attribute instead of a
colorsproperty; additional polishing of multi-class colors in
KElbowVisualizerfit method and quick method to allow passing
sample_weightparameter through the visualizer.
- Enhancements to classification documentation to better discuss precision and recall and to diagnose with
- Improvements to
KElbowVisualizerlabel and legend formatting.
- Typo fixes to
ROCAUCdocumentation, labels, and legend. Typo fix to
- Use of
tight_layoutaccessing the Visualizer figure property to finalize images and resolve discrepancies in plot directive images in documentation.
get_param_nameshelper function to identify keyword-only parameters that belong to a specific method.
- Splits package namespace for
PredictionErrorto its own module,
- Update tests to use
KMeansscores 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
- Fixes to some deprecation warnings from
- 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.