100 Days of Machine Learning | CampusX - Stacking and Blending Ensembles

In this video, we'll try to understand the concepts of stacking and blending ensembles, powerful techniques to enhance model performance in machine learning. We'll explain how combining multiple models strategically can lead to improved predictions and showcase their application in real-world scenarios. Code used: https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day68-stacking-and-blending ============================ 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 E-mail us at [email protected] ✨ Hashtags✨ #EnsembleLearning #ModelStacking #BlendingEnsembles #BoostingPerformance #MachineLearningTechniques #ModelEnsemble ⌚Time Stamps⌚ 00:00 - Intro 00:40 - What is Stacking? 09:25 - The problem with stacking 10:35 - Solutions 12:05 - Blending - using Hold Out Approach 17:44 - Stacking - K Fold Approach 25:32 - Multi Layer Stacking 31:28 - SKLearn implementation / Code Demo