In machine learning, there are various techniques for building models. One of them is ensemble learning that uses a combination of models to achieve better performance. By aggregating different predictions, ensemble models can demonstrate better results than their alternatives.
In this blog post, we will talk about how ensemble learning works, review different types of ensemble learning algorithms, and learn about advantages and disadvantages of ensemble learning for various tasks.
This article is part of our series dedicated to types of machine learning.
Read our other articles for more context:
What Is Unsupervised Learning?
What Is Semi-Supervised Learning?
Reinforcement Learning: How It Works
What is ensemble learning?
Ensemble learning is a machine learning paradigm that proposes to use multiple models to create a stronger model. The fundamental idea behind ensemble learning is that a group of models, when working together, can outperform any individual model. It is a lot like teamwork in a corporation: a team of specialists can potentially come up with a more qualified solution than an individual employee.
