Gradient Boosting and XGBoost in Machine Learning: Easy Explanation for Data Science Interviews

Questions about Gradient Boosting frequently appear in data science interviews. In this video, I cover what the Gradient Boosting method and XGBoost are, teach you how I would describe the architecture of gradient boosting, and go over some common pros and cons associated with gradient-boosted trees. 🟢Get all my free data science interview resources https://www.emmading.com/resources 🟡 Product Case Interview Cheatsheet https://www.emmading.com/product-case-cheat-sheet 🟠 Statistics Interview Cheatsheet https://www.emmading.com/statistics-interview-cheat-sheet 🟣 Behavioral Interview Cheatsheet https://www.emmading.com/behavioral-interview-cheat-sheet 🔵 Data Science Resume Checklist https://www.emmading.com/data-science-resume-checklist ✅ We work with Experienced Data Scientists to help them land their next dream jobs. Apply now: https://www.emmading.com/coaching // Comment Got any questions? Something to add? Write a comment below to chat. // Let's connect on LinkedIn: https://www.linkedin.com/in/emmading001/ ==================== Contents of this video: ==================== 00:00 Introduction 01:01 Gradient Boosting 02:11 Gradient-boosted Trees 02:54 Algorithm 05:53 Hyperparameters 07:55 Pros and Cons 09:00 XGBoost