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@rebeccabilbro rebeccabilbro released this Nov 14, 2018 · 272 commits to develop since this release

Deployed: Wednesday, November 14, 2018
Contributors: @rebeccabilbro, @bbengfort, @zjpoh, @Kautumn06, @ndanielsen, @drwaterman, @lwgray, @pdamodaran, @Juan0001, @abatula, @peterespinosa, @jlinGG, @rlshuhart, @archaeocharlie, @dschoenleber, @black-tea, @iguk1987, @mohfadhil, @lacanlale, @agodbehere, @sivu1, @gokriznastic

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/ to tests/ 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.

Assets 2