Yellowbrick v0.9

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:

- Target module added for visualizing dependent variable in supervised models.
- Added a prototype for a missing values visualizer to the contrib module.
- BalancedBinningReference visualizer for thresholding unbalanced data (undocumented).
- CVScores visualizer to instrument cross-validation.
- FeatureCorrelation visualizer to compare relationship between a single independent variable and the target.
- ICDM visualizer, intercluster distance mapping using projections similar to those used in pyLDAVis.
- PrecisionRecallCurve visualizer showing the relationship of precision and recall in a threshold-based classifier.
- Enhanced FeatureImportance for multi-target and multi-coefficient models (e.g probabilistic models) and allows stacked bar chart.
- Adds option to plot PDF to ResidualsPlot histogram.
- Adds document boundaries option to DispersionPlot and uses colored markers to depict class.
- Added alpha parameter for opacity to the scatter plot visualizer.
- Modify KElbowVisualizer to accept a list of k values.
- ROCAUC bugfix to allow binary classifiers that only have a decision function.
- TSNE bugfix so that title and size params are respected.
- ConfusionMatrix bugfix to correct percentage displays adding to 100.
- ResidualsPlot bugfix to ensure specified colors are both in histogram and scatterplot.
- Fixed unicode decode error on Py2 compatible Windows using Hobbies corpus.
- Require matplotlib 1.5.1 or matplotlib 2.0 (matplotlib 3.0 not supported yet).
- Yellowbrick now depends on SciPy 1.0 and scikit-learn 0.20.
- Deprecated percent and sample_weight arguments to ConfusionMatrix fit method.

Minor Changes:

- Removed hardcoding of SilhouetteVisualizer axes dimensions.
- Audit classifiers to ensure they conform to score API.
- Fix for Manifold fit_transform bug.
- Fixed Manifold import bug.
- Started reworking datasets API for easier loading of examples.
- Added Timer utility for keeping track of fit times.
- Added slides to documentation for teachers teaching ML/Yellowbrick.
- Added an FAQ to the documentation.
- Manual legend drawing utility.
- New examples notebooks for Regression and Clustering.
- Example of interactive classification visualization using ipywidgets.
- Example of using Yellowbrick with PyTorch.
- Repairs to ROCAUC tests and binary/multiclass ROCAUC construction.
- Rename tests/random.py to tests/rand.py to prevent NumPy errors.
- Improves ROCAUC, KElbowVisualizer, and SilhouetteVisualizer documentation.
- Fixed visual display bug in JointPlotVisualizer.
- Fixed image in JointPlotVisualizer documentation.
- Clear figure option to poof.
- Fix color plotting error in residuals plot quick method.
- Fixed bugs in KElbowVisualizer, FeatureImportance, Index, and Datasets documentation.
- Use LGTM for code quality analysis (replacing Landscape).
- Updated contributing docs for better PR workflow.
- Submitted JOSS paper.