40. Entropy and Information Gain

Описание: 📌 Video Description

Entropy and Information Gain form the mathematical foundation of decision trees in machine learning and data science. In this video, we explain how decision trees decide where and how to split data using precise mathematical measures.

You’ll learn how entropy measures the amount of randomness or uncertainty in a dataset, and how information gain quantifies the reduction in that randomness after a split. Decision trees choose splits that maximize information gain, leading to purer, more informative branches and better predictions.

This lesson focuses on intuition first—helping you understand why the math works, not just how to compute it.

🎯 What You’ll Learn in This Video

Why entropy and information gain matter

What entropy measures in a dataset

Understanding randomness and uncertainty

What information gain represents

How decision trees choose the best splits

Relationship between entropy and tree depth

Why information gain improves model accuracy

👨‍💻 Who This Video Is For

Data science and machine learning beginners

Students preparing for exams

Data analysts and ML practitioners

Anyone learning how decision trees work internally

🔑 Keywords (SEO)

Entropy and Information Gain, Decision Trees, Machine Learning Mathematics, Entropy Explained, Information Gain Explained, Tree Based Models, Data Science Fundamentals, Learn Decision Trees

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#Entropy #InformationGain #DecisionTrees #MachineLearning #DataScience #LearnDataScience #TechFundamentals