CampusX Data Science Mentorship Program 2022-23 - The Maths Behind XGBoost | Machine Learning | CampusX

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What you'll learn

This course includes

365.3 hours of video
Certificate of completion
Access on mobile and TV

Summary

Keywords

Education Engineering Campus Placement Skills Machine Learning Software Profile Building XGBoost Extreme Gradient Boosting Ensemble Learning Machine Learning Algorithm

Full Transcript

In this tutorial, we unravel the intricate details of XGBoost, from boosting as an additive model to the derivation of the loss function, addressing challenges with the objective function, and introducing the solution using the Taylor Series. Whether you're a data science enthusiast, a student, or a seasoned practitioner, join us as we break down the complex mathematics behind XGBoost. 📈 Original Research Paper: https://browse.arxiv.org/pdf/1603.02754.pdf 🔗 XgBoost Series Videos: XgBoost Introduction: https://www.youtube.com/watch?v=C6aDw4y8qJ0 XgBoost For Regression: https://www.youtube.com/watch?v=gmp2tS2joaA XgBoost For Classification: https://www.youtube.com/watch?v=mELtxVUNNrw Maths Behind XgBoost: https://www.youtube.com/watch?v=0Eo-_5bfers ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 👍If you find this video helpful, consider giving it a thumbs up and subscribing for more educational videos on data science! Share your thoughts, experiences, or questions in the comments below. I love hearing from you! ⌚Time Stamps⌚ 00:00 - Intro 01:44 - The Plan 06:07 - Pre-requisite 07:12 - Boosting as an Additive Model 15:54 - XGBoost Loss Function 26:19 - The Derivation 37:01 - Taylor Series Expansion 50:04 - The solution to Taylor series 58:51 - Applying Taylor Series 01:06:00 - Simplification 01:29:08 - Output Value for Regression 01:36:43 - Output Value for Classification 01:39:52 - Derivation of Similarity Score 01:45:40 - Final Calculation of Similarity Score 01:56:56 - Outro ✨ Hashtags✨ #DataScience #MachineLearning #XGboost #CampusX #XGBoostMathematics #BoostingAlgorithm #AlgorithmDerivation #TaylorSeries #AIExplained