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C4.5 Decision Tree Algorithm in Python

C4.5 is one of the most common decision tree algorithm. It offers some improvements over ID3 such as handling numerical features. It uses entropy and gain ratio 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 C4.5 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/2018/05/13/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   Threads: https://www.threads.net/@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