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yellowbrick 1.5
0
A suite of visual analysis and diagnostic tools for machine learning.
Contents
A suite of visual analysis and diagnostic tools for machine learning.
Stars: 4251, Watchers: 4251, Forks: 554, Open Issues: 97The DistrictDataLabs/yellowbrick
repo was created 8 years ago and the last code push was 12 months ago.
The project is very popular with an impressive 4251 github stars!
How to Install yellowbrick
You can install yellowbrick using pip
pip install yellowbrick
or add it to a project with poetry
poetry add yellowbrick
Package Details
- Author
- The scikit-yb developers
- License
- Apache 2
- Homepage
- http://scikit-yb.org/
- PyPi:
- https://pypi.org/project/yellowbrick/
- Documentation:
- http://scikit-yb.org/
- GitHub Repo:
- https://github.com/DistrictDataLabs/yellowbrick
Classifiers
- Scientific/Engineering/Visualization
- Software Development
- Software Development/Libraries/Python Modules
Related Packages
Errors
A list of common yellowbrick errors.
Code Examples
Here are some yellowbrick
code examples and snippets.
GitHub Issues
The yellowbrick package has 97 open issues on GitHub
- Learning Curve Documentation
- BUG: Corrects legend issues other than R2 in PredictionError
- Diagnostic Plots for Linear Regression Analysis
- BUG: Adds missing X and Y axes labels in ClassificationReport
- Argument to PredictionError is overwritten
- BUG: Fixes axes limit for PredictionError plot #1193
- Random input feature dropping curve, model selection visualization [issue #1024]
- Gap Statistic and Davies-Bouldin Index
- Model selection curve for Random Input Dropout
- AttributeError: 'KMeans' object has no attribute 'k'
- PredictionError plot forces min/max limits on the graph that are unhelpful if you have lots of tiny errors
- PredictionError hard codes score legend to R2 even when other scoring functions are used