Metadata-Version: 2.1
Name: dtreeviz
Version: 2.1.4
Summary: A Python 3 library for sci-kit learn, XGBoost, LightGBM, Spark, and TensorFlow decision tree visualization
Home-page: https://github.com/parrt/dtreeviz
Author: Terence Parr, Tudor Lapusan, and Prince Grover
Author-email: parrt@antlr.org
License: MIT
Keywords: machine-learning data structures trees visualization
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Requires-Python: >=3.6
Provides-Extra: xgboost
Provides-Extra: pyspark
Provides-Extra: lightgbm
Provides-Extra: tensorflow_decision_forests
Provides-Extra: all
License-File: LICENSE

A python library for decision tree visualization and model interpretation.  Decision trees are the fundamental building block of [gradient boosting machines](http://explained.ai/gradient-boosting/index.html) and [Random Forests](https://en.wikipedia.org/wiki/Random_forest)(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](http://www.r2d3.us/); [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/). Please see [How to visualize decision trees](http://explained.ai/decision-tree-viz/index.html) for deeper discussion of our decision tree visualization library and the visual design decisions we made.

Currently dtreeviz supports: [scikit-learn](https://scikit-learn.org/stable), [XGBoost](https://xgboost.readthedocs.io/en/latest), [Spark MLlib](https://spark.apache.org/mllib/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [Tensorflow](https://www.tensorflow.org/decision_forests).  See [Installation instructions](README.md#Installation).
