A python library for decision tree visualization and model interpretation. Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. The visualizations are inspired by an educational animation by R2D3; A visual introduction to machine learning. Please see How to visualize decision trees for deeper discussion of our decision tree visualization library and the visual design decisions we made.

Features

  • Supports scikit-learn, XGBoost, Spark MLlib, LightGBM, and Tensorflow. See Installation instructions
  • Sample Visualizations
  • Prediction path explanations
  • Leaf information
  • Documentation available
  • Verify graphviz installation
  • Install dtreeviz locally

Project Samples

Project Activity

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Categories

Machine Learning

License

MIT License

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Additional Project Details

Operating Systems

Linux, Mac, Windows

Registered

2024-08-06