Yellowbrick v0.9
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:
- 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.
Files
yellowbrick-0.9.zip
Files
(29.8 MB)
Name | Size | Download all |
---|---|---|
md5:999fcc5a9fa8b70b1ee657bc1575cac1
|
29.8 MB | Preview Download |
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)