Ensemble Learning | Ensemble Learning In Machine Learning | Machine Learning | Intellipaat
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In this video, you will learn about one of the popular machine learning techniques - "Ensemble Learning." This video on Ensemble Learning will teach you what Ensemble Learning is and how it works in a simplified manner. 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. You will also get to know about Bagging, Boosting, and Stacking. So, please stay tuned with us until the end of this video.
🔵 Following topics are covered in this session:
00:00 - Introduction
00:44 - What is Ensemble Learning?
02:36 - How does ensemble learning work?
04:20 - Benefits of using Ensemble Learning
05:13 - Types of Ensemble Learning
06:50 - Applications of Ensemble Learning
07:34 - 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 ensemble machine learning?
Ensemble 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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