This release is the culmination of the Spring 2017 DDL Research Labs that focused on developing Yellowbrick as a community effort guided by a sprint/agile workflow. We added several more visualizers, did a lot of user testing and bug fixes, updated the documentation, and generally discovered how best to make Yellowbrick a friendly project to contribute to.
Notable in this release is the inclusion of two new feature visualizers that use few, simple dimensions to visualize features against the target. The
JointPlotVisualizer graphs a scatter plot of two dimensions in the data set and plots a best fit line across it. The
ScatterVisualizer also uses two features, but also colors the graph by the target variable, adding a third dimension to the visualization.
This release also adds support for clustering visualizations, namely the elbow method for selecting K,
KElbowVisualizer and a visualization of cluster size and density using the
SilhouetteVisualizer. The release also adds support for regularization analysis using the
AlphaSelection visualizer. Both the text and classification modules were also improved with the inclusion of the
PosTagVisualizer and the
ConfusionMatrix visualizer respectively.
This release also added an Anaconda repository and distribution so that users can
conda install yellowbrick. Even more notable, we got yellowbrick stickers! We've also updated the documentation to make it more friendly and a bit more visual; fixing the API rendering errors. All-in-all, this was a big release with a lot of contributions and we thank everyone that participated in the lab!
- Part of speech tags visualizer --
- Alpha selection visualizer for regularized regression --
- Confusion Matrix Visualizer --
- Elbow method for selecting K vis --
- Silhouette score cluster visualization --
- Joint plot visualizer with best fit --
- Scatter visualization of features --
- Added three more example datasets: mushroom, game, and bike share
- Contributor's documentation and style guide
- Maintainers listing and contacts
- Light/Dark background color selection utility
- Structured array detection utility
- Updated classification report to use colormesh
- Added anacondas packaging and distribution
- Refactoring of the regression, cluster, and classification modules
- Image based testing methodology
- Docstrings updated to a uniform style and rendering
- Submission of several more user studies