Decision Tree Entropy & Information Gain Explained Visually | Part 2

How does a Decision Tree decide which feature to split on? In this video, we explain Entropy and Information Gain step by step, using visual examples, mathematical calculations and a simulated diabetes dataset. Welcome to The Data Helix — where we explore Artificial Intelligence, Machine Learning, Data Science, Statistics and Genomics through clear explanations and practical examples. 🌳 What you'll learn: ✅ Recap of Decision Trees and Gini Impurity from Part 1 ✅ What entropy means and how it measures uncertainty ✅ Calculating entropy by hand using class probabilities ✅ Understanding pure and mixed nodes ✅ Information Gain: entropy before and after splitting ✅ Comparing Family History, Exercise and BMI as predictors ✅ Choosing the best feature and split threshold ✅ Growing a Decision Tree step by step ✅ Making predictions using a trained Decision Tree ✅ Why ID-like features can produce misleadingly high Information Gain ✅ Understanding Gain Ratio and overfitting ✅ Gini Impurity vs Entropy: similarities and differences Entropy formula: H = −Σ pᵢ log₂(pᵢ) Information Gain: IG = Parent Entropy − Weighted Child Entropy We use a small simulated dataset containing family history, exercise habits, BMI and diabetes status to demonstrate these concepts visually. Watch Part 1 — Decision Tree with Gini Impurity:    • Decision Tree Explained with Gini Impurity...   Who should watch? This lecture is suitable for beginners, Data Science students, Machine Learning learners, researchers and anyone preparing for technical interviews. Important: The dataset is simulated for educational purposes and should not be interpreted as a clinical diabetes prediction model. Subscribe to The Data Helix for detailed tutorials on Machine Learning, Deep Learning, Generative AI, Agentic AI, Statistics, Bioinformatics and Computational Genomics. #DecisionTree #Entropy #InformationGain #MachineLearning #DataScience #TheDataHelix