Yellowbrick v1.2
Description
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 withpredict_proba
anddecision_function
methods are handling. Abinary
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 aclass_colors_
learned attribute instead of acolors
property; additional polishing of multi-class colors inPrecisionRecallCurve
,ROCAUC
, andClassPredictionError
. -
Update
KElbowVisualizer
fit method and quick method to allow passingsample_weight
parameter through the visualizer. -
Enhancements to classification documentation to better discuss precision and recall and to diagnose with
PrecisionRecallCurve
andClassificationReport
visualizers. -
Improvements to
CooksDistance
visualizer documentation. -
Corrected
KElbowVisualizer
label and legend formatting. -
Typo fixes to
ROCAUC
documentation, labels, and legend. Typo fix toManifold
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 movePredictionError
to its own module,yellowbrick.regressor.prediction_error
. -
Update tests to use
SVC
instead ofLinearSVC
and correctKMeans
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.
Files
yellowbrick-1.2.zip
Files
(59.7 MB)
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md5:3fcac76039a49ce81f21e59c0743947f
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Additional details
Related works
- Is documented by
- http://www.scikit-yb.org/en/stable/ (URL)
- Is supplemented by
- https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.6 (URL)