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.