disparate machine learning models

The principle of “the wisdom of the crowd” shows that a large group of people with average knowledge on a topic can provide reliable answers to questions such as predicting quantities, spatial reasoning, and general knowledge. The aggregate results cancel out the noise and can often be superior to those of highly knowledgeable experts. The same rule can apply to artificial intelligence applications that rely on machine learning, the branch of AI that predicts outcomes based on mathematical models.

In machine learning, crowd wisdom is achieved through ensemble learning. For many problems, the result obtained from an ensemble, a combination of machine learning models, can be more accurate than any single member of the group.

How does ensemble learning work?

Say you want to develop a machine learning model that predicts inventory stock orders for your company based on historical data you have gathered from previous years. You use train four machine learning models using a different algorithms: linear regression, support vector machine, a regression decision tree, and a basic artificial neural network. But even after much tweaking and configuration, none of them achieves your desired 95 percent prediction accuracy. These machine learning models are called “weak learners” because they fail to converge to the desired level.