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| Name | Ensemble Machine Learning Techniques |
| Video Uploader | Video From Six Sigma Pro SMART |
| Upload Date | This Video Uploaded At 05-04-2024 13:00:43 |
| Video Discription | In this video, we cover the very important topic of ensembling in machine learning! Starting with the dictionary meaning of "ensemble," we explore why ensembling is crucial by understanding the bias-variance trade-off and how ensembling helps achieve low bias and low variance for better model performance. Bias variance trade-off - https://youtu.be/bM6jcoyK2ps Complete supervised learning - https://tinyurl.com/33wruyd8 🔍 Next, we navigate through a mind map of ensembling techniques, classifying them into three main categories: Parallel, Sequential, and Composite. We explain each technique with two perspectives: base models (same or different) and learning method (sequential or parallel). Parallel Techniques: Bagging: Same models, different data, learn in parallel. Voting: Different models, different data, learn in parallel. Sequential Techniques: Boosting: Same model, learns sequentially, correcting errors. Composite Techniques: Blending: Different models, learns both sequentially and in parallel. Stacking: Different models, learns both sequentially and in parallel. Join us as we explain ensembling and how it can elevate your machine learning models to new heights! Don't forget to like, share, and subscribe for more insightful content! 🎓 mGWRxM-MUN4 |
| Category | Education |
| Tags | Education Download MP4 | Education Download MP3 | Education Download MP4 360p | Education Download MP4 480p | Education Download MP4 720p | Education Download MP4 1080p |
