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In this video on Ensemble Learning in Machine Learning, you will learn about one of the technique that helps improve the accuracy by merging more than one machine learning algorithms. This newly formed algorithm is termed as ensemble and the method itself is termed as ensemble learning in machine learning. This video will take you through the intuitive exploration of Ensemble Learning and how it works. Additionally, the video will cover the types of ensemble learning, the benefits of using ensemble learning over other machine learning models, and some applications of ensemble learning.

Following topics are covered in this Ensemble Learning Tutorial:
00:00 - Introduction
00:44 - What is Ensemble Learning Explained with Example
02:05 - Ensemble Learning Definition
02:38 - How Ensemble Learning Works?
04:12 - Weak Learners Form Strong Learners
04:25 - Benefits of Using Ensemble Learning
05:13 - Types of Ensemble Learning
05:29 - Bagging
05:57 - Boosting
06:25 - Stacking
06:50 - Applications of Ensemble Learning
07:24 - Conclusion

What is ensemble in machine learning?
Ensemble learning in machine learning involves combining predictions from multiple models to enhance overall performance. By aggregating diverse models, such as decision trees, it mitigates individual weaknesses, leading to more accurate and robust predictions. Common techniques include Bagging (Bootstrap Aggregating), Boosting, and Stacking, contributing to improved model generalization and predictive power in various applications.

Why to use ensemble learning in
machine learning?
Ensemble Learning in machine learning enhances model accuracy and robustness by combining predictions from multiple diverse models. It mitigates individual model weaknesses, reducing overfitting and improving generalization.

What are the three types of ensemble learning?
The three types of ensemble learning are Bagging, Boosting, and Stacking. Bagging creates diverse models by training them on random subsets of the dataset, reducing overfitting. Boosting emphasizes misclassified instances, sequentially improving model performance. Stacking combines predictions from multiple models, using a meta-model to achieve enhanced predictive accuracy and robustness.

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