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CART (Classification and Regression Trees) is one of the most common decision tree algorithm. It can handle numerical features. It uses gini index to find the decision points in the decision tree. Herein, chefboost is a python based gradient boosting, random forest and adaboost enabled decision tree framework. In this video, we are going to see how to apply CART for a classification problem in Python. You need to write just a few lines of code. Framework: https://github.com/serengil/chefboost Documentation: https://sefiks.com/2018/08/27/a-step-... Videos for Python Implementations of Regular Decision Tree Algorithms CHAID: • CHAID Decision Tree Algorithm in Python ID3: • ID3 Decision Tree Algorithm in Python C4.5: • C4.5 Decision Tree Algorithm in Python CART: • CART Decision Tree Algorithm in Python Regression Trees: • Regression Trees and Decision Trees i... Videos for Overview of Regular Decision Tree Algorithms CHAID: • The Math Behind CHAID Decision Tree A... ID3: • The Math Behind ID3 Decision Tree Alg... C4.5: • The Math Behind C4.5 Decision Tree Al... CART: • The Math Behind CART Decision Tree Al... Regression Trees: • The Math Behind Regression Trees in D... Please Subscribe! That's what keeps me going ► https://bit.ly/40NfIS7 Want more? Connect with me here: Blog: https://sefiks.com/ Twitter: / serengil Instagram: / serengil Facebook: / sefikscom Linkedin: / serengil If you do like my videos, you can support my effort with your financial contributions on Patreon: https://www.patreon.com/serengil?sour... GitHub Sponsors: https://github.com/sponsors/serengil Buy Me a Coffee: https://buymeacoffee.com/serengil