Decision Tree Learning

This page contains resources about Decision Tree Learning.

Subfields and Concepts

 * Decision trees
 * Decision forests / Random forests

Books and Book Chapters

 * Criminisi, A., & Shotton, J. (2013). Decision Forests for Computer Vision and Medical Image Analysis. Springer.
 * Russell, S. J., & Norvig, P. (2010). "Section 18.3: Learning Decision Trees". Artificial Intelligence: A Modern Approach. Prentice Hall.
 * Mitchell, T. M. (1997). "Chapter 3: Decision Tree Learning". Machine Learning. McGraw Hill.

Software

 * Classification Trees (Statistics and Machine Learning) - MATLAB
 * Regression Trees (Statistics and Machine Learning) - MATLAB
 * Regression Tree Ensembles (Statistics and Machine Learning) - MATLAB
 * Decision Trees (scikit-learn) - Python
 * catboost - Python
 * LightGBM - Python
 * XGBoost - Python
 * GradientBoostingClassifier (scikit-learn) - Python
 * Sherwood - C++ and C# library for Decision Forests

Other Resources

 * Awesome-Random-Forest (Github) - A curated list of resources
 * Feature transformations with ensembles of trees - sklearn