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A suite of visual analysis and diagnostic tools for machine learning.

Project Description

Yellowbrick is a suite of visual analysis and diagnostic tools designed to facilitate machine learning with Scikit-Learn. The package includes visualizations that can help users navigate the feature selection process, build intuition around model selection, diagnose common problems like bias, heteroscedasticity, underfit, and overtraining, and support hyperparameter tuning to steer predictive models toward more successful results.

Some of the available tools include:

  • histograms
  • scatter plot matrices
  • parallel coordinates
  • jointplots
  • ROC curves
  • classification heatmaps
  • residual plots
  • validation curves
  • gridsearch heatmaps

For more, please see the full documentation at:

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yellowbrick-0.1-py2-none-any.whl (7.8 kB) Copy SHA256 Checksum SHA256 py2 Wheel May 18, 2016
yellowbrick-0.1.tar.gz (5.6 kB) Copy SHA256 Checksum SHA256 Source May 18, 2016

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