Random Forest Decision Trees in Python

Random forest is a hot topic in decision trees. It is a bagging method. It separates the data set into several sub data set and applies regular decision tree algorithms (e.g. ID3 or C4.5) onto these sub data set. In this way, it avoids overfitting and performs faster than regular decision tree algorithms. 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 random forest 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/2017/11/19/how-random-forests-can-keep-you-from-decision-tree/ 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