Machine Learning

How To Implement The Decision Tree Algorithm From Scratch In Python

Last Updated on December 11, 2019

Decision trees are a powerful prediction method and extremely popular.

They are popular because the final model is so easy to understand by practitioners and domain experts alike. The final decision tree can explain exactly why a specific prediction was made, making it very attractive for operational use.

Decision trees also provide the foundation for more advanced ensemble methods such as bagging, random forests and gradient boosting.

In this tutorial, you will discover how to implement the Classification And Regression Tree algorithm from scratch with Python.

After completing this tutorial, you will know:

  • How to calculate and evaluate candidate split points in a data.
  • How to arrange splits into a decision tree structure.
  • How to apply the classification and regression tree algorithm to a real problem.

Kick-start your project with my new book Machine Learning Algorithms From Scratch, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Update Jan/2017: Changed the calculation of fold_size in cross_validation_split() to always be an integer. Fixes issues with Python 3.
  • Update Feb/2017: Fixed a bug in build_tree.
  • Update Aug/2017: Fixed a bug in Gini calculation, added the missing
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