1488364
doi
10.5281/zenodo.1488364
oai:zenodo.org:1488364
user-ddl
Bilbro, Rebecca
Danielsen, Nathan
Gray, Larry
McIntyre, Kristen
Roman, Prema
Poh, Zijie (ZJ)
Waterman, David
Kehoe, Juan
Batula, Alyssa
Espinosa, Peter
Lin, Joanne
Black, Tim
Fadhil, Mohammed
Lacanlale, Jonathan
Godbehere, Andrew
Santhanam, Sivasurya
Krishna, Gopal
Yellowbrick v0.9
Bengfort, Benjamin
url:https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.6
url:http://www.scikit-yb.org/en/stable/
info:eu-repo/semantics/openAccess
Apache License 2.0
http://www.apache.org/licenses/LICENSE-2.0
matplotlib
sckit-learn
machine learning
visualization
python
<p>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.</p>
<p><strong>Major Changes:</strong></p>
<p>- <code>Target</code> module added for visualizing dependent variable in supervised models.<br>
- Added a prototype for a missing values visualizer to the <code>contrib</code> module.<br>
- <code>BalancedBinningReference</code> visualizer for thresholding unbalanced data (undocumented).<br>
- <code>CVScores</code> visualizer to instrument cross-validation.<br>
- <code>FeatureCorrelation</code> visualizer to compare relationship between a single independent variable and the target.<br>
- <code>ICDM</code> visualizer, intercluster distance mapping using projections similar to those used in pyLDAVis.<br>
- <code>PrecisionRecallCurve</code> visualizer showing the relationship of precision and recall in a threshold-based classifier.<br>
- Enhanced <code>FeatureImportance</code> for multi-target and multi-coefficient models (e.g probabilistic models) and allows stacked bar chart.<br>
- Adds option to plot PDF to <code>ResidualsPlot</code> histogram.<br>
- Adds document boundaries option to <code>DispersionPlot</code> and uses colored markers to depict class.<br>
- Added alpha parameter for opacity to the scatter plot visualizer.<br>
- Modify <code>KElbowVisualizer</code> to accept a list of k values.<br>
- <code>ROCAUC</code> bugfix to allow binary classifiers that only have a decision function.<br>
- <code>TSNE</code> bugfix so that title and size params are respected.<br>
- <code>ConfusionMatrix</code> bugfix to correct percentage displays adding to 100.<br>
- <code>ResidualsPlot</code> bugfix to ensure specified colors are both in histogram and scatterplot.<br>
- Fixed unicode decode error on Py2 compatible Windows using Hobbies corpus.<br>
- Require matplotlib 1.5.1 or matplotlib 2.0 (matplotlib 3.0 not supported yet).<br>
- Yellowbrick now depends on SciPy 1.0 and scikit-learn 0.20.<br>
- Deprecated <code>percent</code> and <code>sample_weight</code> arguments to <code>ConfusionMatrix</code> <code>fit</code> method.</p>
<p><strong>Minor Changes:</strong></p>
<p>- Removed hardcoding of <code>SilhouetteVisualizer</code> axes dimensions.<br>
- Audit classifiers to ensure they conform to score API.<br>
- Fix for <code>Manifold</code> <code>fit_transform</code> bug.<br>
- Fixed <code>Manifold</code> import bug.<br>
- Started reworking datasets API for easier loading of examples.<br>
- Added Timer utility for keeping track of fit times.<br>
- Added slides to documentation for teachers teaching ML/Yellowbrick.<br>
- Added an FAQ to the documentation.<br>
- Manual legend drawing utility.<br>
- New examples notebooks for Regression and Clustering.<br>
- Example of interactive classification visualization using ipywidgets.<br>
- Example of using Yellowbrick with PyTorch.<br>
- Repairs to <code>ROCAUC</code> tests and binary/multiclass ROCAUC construction.<br>
- Rename tests/random.py to tests/rand.py to prevent NumPy errors.<br>
- Improves <code>ROCAUC</code>, <code>KElbowVisualizer</code>, and <code>SilhouetteVisualizer</code> documentation.<br>
- Fixed visual display bug in <code>JointPlotVisualizer</code>.<br>
- Fixed image in <code>JointPlotVisualizer</code> documentation.<br>
- Clear figure option to poof.<br>
- Fix color plotting error in residuals plot quick method.<br>
- Fixed bugs in <code>KElbowVisualizer</code>, <code>FeatureImportance</code>, Index, and Datasets documentation.<br>
- Use LGTM for code quality analysis (replacing Landscape).<br>
- Updated contributing docs for better PR workflow.<br>
- Submitted JOSS paper.</p>
Zenodo
2018-11-14
info:eu-repo/semantics/other
1206239
user-ddl
0.9
1661087480.805101
29839841
md5:999fcc5a9fa8b70b1ee657bc1575cac1
https://zenodo.org/records/1488364/files/yellowbrick-0.9.zip
public
https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.6
Is supplemented by
url
http://www.scikit-yb.org/en/stable/
Is documented by
url
10.5281/zenodo.1206239
isVersionOf
doi