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Ensemble learning is the use of algorithms and tools in machine learning and other disciplines, to form a collaborative whole where multiple methods are more effective than a single learning method. Ensemble learning can be used in many different types of research, for flexibility and enhanced results.
Many ensemble learning tools can be trained to produce various results. Individual algorithms may be stacked on top of each other, or rely on a “bucket of models” method of evaluating multiple methods for one system. In some cases, multiple data sets are aggregated and combined. For example, a geographic research program may use multiple methods to assess the prevalence of items in a geographic space. One of the issues with this type of research involves making sure that various models are independent, and that the combination of data is practical and works in a particular scenario.
Ensemble learning methods are included in different types of statistical software packages. Some experts describe ensemble learning as “crowdsourcing” of data aggregation.