What are some of the main benefits of ensemble learning?
Ensemble learning has various benefits for machine learning projects. Many of these are related to using a large number of relatively simple nodes to aggregate some inputs and output results.
For example, ensemble learning can help project managers to deal with both bias and variance — variance representing scattered results that are difficult to converge, and bias representing miscalibration or error in targeting necessary results.
There’s long and involved mathematical analysis of how each of these solutions works, along with various practices like boosting and bagging, but for those who aren’t personally involved in machine learning, it may be enough to understand that ensemble learning basically brings a decentralized, consensus-based approach to machine learning that helps to refine results and ensure precision. Think of ensemble learning as the essential “crowdsourcing” of points of input in order to come up with a big picture analysis. In a sense, this is what machine learning is all about, and AdaBoost or related systems do this through an ensemble learning approach. Another way to boil this concept down to its basics is to think about the old slogan: “two heads are better than one” and think about how decentralizing sourcing or control helps to come up with more precise results.
One example of ensemble learning is a random forest approach. In a random forest, a group of decision trees has some overlapping material, and some unique results that are blended together to achieve a goal with mathematical and methodical outcome. This is an example of how ensemble learning works practically to support better machine learning in neural networks and other systems. In a basic sense, the data “merges” and is stronger for its decentralized origins.
Tags
Written by Justin Stoltzfus | Contributor, Reviewer

Justin Stoltzfus is a freelance writer for various Web and print publications. His work has appeared in online magazines including Preservation Online, a project of the National Historic Trust, and many other venues.
More Q&As from our experts
- How might companies use random forest models for predictions?
- Why is bias versus variance important for machine learning?
- What is the difference between little endian and big endian?
Related Terms
- Machine Learning
- Ensemble Learning
- Machine Bias
- Boosting
- Bagging
- Crowdsourcing
- AdaBoost
- Random Forest
- Decision Tree
- Artificial Neural Network
Related Articles

11 Quotes About AI That'll Make You Think

New Jobs in the AI Era

Debunking the Top 4 Myths About Machine Learning

Reinforcement Learning: Scaling Personalized Marketing
Tech moves fast! Stay ahead of the curve with Techopedia!
Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia.
- The CIO Guide to Information Security
- Robotic Process Automation: What You Need to Know
- Data Governance Is Everyone's Business
- Key Applications for AI in the Supply Chain
- Service Mesh for Mere Mortals - Free 100+ page eBook
- Do You Need a Head of Remote?
- Web Data Collection in 2022 - Everything you need to know