Gini Impurity vs Entropy in Decision Trees Explained with Real-Life Example | Information Gain

Salaam Alaikum and welcome back to Zero to AI Pro with Zayn Malik!
In this video, we dive deep into one of the most fundamental concepts in Machine Learning and Data Science — the difference between Gini Impurity and Entropy used in Decision Trees and Random Forests.

We’ll break down complex terms like Information Gain, Impurity, and Entropy Formula in a super simple and intuitive way — using a real-world example of colored balls 🎨 to visualize purity and disorder in datasets.

📘 In this session, you’ll learn:

What is Gini Impurity?

What is Entropy in Decision Trees?

How Information Gain decides the best split.

Real-world analogy of purity vs impurity.

The mathematical formulas for Gini and Entropy.

When to use Gini vs Entropy in Machine Learning models.

Understanding how ID3, C4.5, and Scikit-learn handle splitting.

Visual comparison between both measures.

📊 Example covered:
Dataset with 10 samples — 6 of Class A and 4 of Class B.
We calculate both Gini (0.48) and Entropy (0.971) step-by-step to show how splitting decisions are made in Decision Trees.

🔥 Why Watch This Video:
This is not just theory — it’s an easy-to-follow explanation in Urdu/Hindi, perfect for beginners and students preparing for exams or interviews in AI, ML, or Data Science.

📍 Timestamps:
00:00 – Intro: Welcome to Zero to AI Pro
00:11 – Gini Impurity vs Entropy Overview
01:10 – Real-world Example (Colored Balls)
03:00 – Mathematical Formulas Explained
04:37 – Calculating Gini and Entropy
06:18 – Comparison and When to Use Which
07:50 – Implementation Tips (Scikit-learn & ID3/C4.5)
09:30 – Summary and Exam Questions

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Gini impurity vs entropy, information gain, decision tree tutorial, random forest, machine learning in urdu, zero to ai pro, Zayn Malik AI, entropy explained, gini formula, information theory in machine learning, data impurity, ML concepts explained, AI for beginners, data science urdu tutorial

Видео Gini Impurity vs Entropy in Decision Trees Explained with Real-Life Example | Information Gain канала Zero to AI Pro with ZebMalik