Decision Tree Learning
This page contains resources about Decision Tree Learning.
Subfields and Concepts[edit]
- Decision trees
- Decision forests / Random forests
Online Courses[edit]
Video Lectures[edit]
Lectures Notes [edit]
Books and Book Chapters[edit]
- 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.
Scholarly Articles[edit]
Tutorials[edit]
Software[edit]
- 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
See also[edit]
Other Resources[edit]
- Awesome-Random-Forest (Github) - A curated list of resources
- Feature transformations with ensembles of trees - sklearn