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

Описание: 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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