Simplest example of Decision Tree | Entropy | Randomness | Information Gain in Decision Tree

How does a Decision Tree Work? A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Splitting stops when every subset is pure (all elements belong to a single class) Code for visualising a decision tree - https://github.com/bhattbhavesh91/vis... If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. You can find me on: GitHub - https://github.com/bhattbhavesh91 Medium -   / bhattbhavesh91   #Entropy #DecisionTree #InformationGain #Python #machinelearning #python #datascience