CHAID Decision Tree Algorithm in Python

CHAID (chi-square automatic interaction detection) is a conventional decision tree algorithm. It uses chi-square testing value to find the decision splits. This metric is used to find the significance in statistics as well. Similar to ID3 and C4.5, it can be applied for classification problems. In other words, you should feed a data set including categorical target value. Herein, chefboost is a python based gradient boosting, random forest and adaboost enabled decision tree framework supporting regular decision tree algorithms as well such as ID3, C4.5, CART and Regression Trees. In this video, we are going to see how to build CHAID decision trees for a classification problem in Python. You need to write just a few lines of code. All you need is to call "pip install chefboost" command. Framework: https://github.com/serengil/chefboost Technical tutorial: https://sefiks.com/2020/03/18/a-step-by-step-chaid-decision-tree-example/ Videos for Python Implementations of Regular Decision Tree Algorithms CHAID: https://youtu.be/dcnFuS4QILg ID3: https://youtu.be/Z93qE5eb6eg C4.5: https://youtu.be/kjhQHmtDaAA CART: https://youtu.be/CSApBetgukM Regression Trees: https://youtu.be/pCQ2RCa20Bg Videos for Overview of Regular Decision Tree Algorithms CHAID: https://youtu.be/cu0ZdZNCbKo ID3: https://youtu.be/NF9Z3oECvaA C4.5: https://youtu.be/344jy4Stusg CART: https://youtu.be/NtQpKgkkdsQ Regression Trees: https://youtu.be/pQOZ1ytAkg4 Please Subscribe! That's what keeps me going ► https://bit.ly/40NfIS7 Want more? Connect with me here: Blog: https://sefiks.com/ Twitter: https://twitter.com/serengil Instagram: https://www.instagram.com/serengil Facebook: https://www.facebook.com/sefikscom Linkedin: https://www.linkedin.com/in/serengil/ If you do like my videos, you can support my effort with your financial contributions on - Patreon: https://www.patreon.com/serengil?source=youtube - GitHub Sponsors: https://github.com/sponsors/serengil - Buy Me a Coffee: https://buymeacoffee.com/serengil