Скачать или смотреть Ensemble Learning Techniques in Machine Learning | Bootstrap Aggregataion (Bagging) vs Boosting

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.

In this video, we will be talking about Ensemble Learning, when & where to use them, and the two different types of Ensemble Learning, along with basic differences in various algorithms. At the end of the lecture, we will also go through a practical code for better understanding.

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✅ 00:00 Introduction
✅ 00:13 What is Ensemble Learning?
✅ 01:14 What are Standalone Models?
✅ 04:25 Why do we use Ensemble Learning?
✅ 05:22 When & Where do we use Ensemble Learning?
✅ 06:20 Types of Ensemble Learning: Bootstrap Aggregation (Bagging) & Boosting
✅ 06:44 Bootstrap Aggregation (Bagging)
✅ 10:12 Boosting
✅ 15:50 Practical Implementation: Codes

🔴 Code: https://bit.ly/3sRE1zT

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