XGBoost Explained: How the Algorithm Actually Works (Step-by-Step)

In this video, Dr. Ghanta breaks down Extreme Gradient Boosting (XGBoost), one of the most powerful and popular algorithms in machine learning.

You’ll learn the core logic of XGBoost, not just what it is, but how it thinks. We go under the hood to see how it builds trees sequentially, learns from its mistakes (residuals), and uses "extreme" features like regularization to get highly accurate results.

Whether you’re a programmer new to AI or brushing up on your data science fundamentals, this clear, intuitive explanation will help you understand how gradient boosting, learning rates, and regularization come together to create the XGBoost algorithm.

📌 In this video, you’ll learn
00:00 – What is XGBoost?
00:44 – The Basics: Classification vs. Regression
01:18 – How Boosting is Different from Random Forest
02:12 – The 4 Core Components of XGBoost
02:47 – Part 1: Gradient Boosting (Building the First Tree)
03:12 – Calculating Residuals, Similarity Score & Gain
06:04 – Part 2: Learning Rate (Eta / Shrinkage) Explained
09:00 – How XGBoost Learns (Building the Second Tree)
10:10 – Part 3: Tree Capacity & Stopping (Gamma & Max Depth)
11:02 – Part 4: Regularization (Lambda) Explained
12:39 – Summary: What Makes it "Extreme"?
13:29 – Wrap-up & Subscribe


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👩‍🏫 About the Presenter:
Dr. Sindhu Ghanta delivers clear, practical, and mathematically intuitive explanations for complex machine learning algorithms. Her/Our style? No jargon. Just clear, useful explanations that help you learn fast and apply your skills immediately.

🚀 Who this is for:
Students, professionals, and AI enthusiasts learning Machine Learning, Data Science, or Artificial Intelligence, and want to understand how Random Forests power real-world predictions.

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