Ensemble Learning - Combining Model Performances

David Landup
David Landup

Ensemble Learning

Ensemble Learning is a powerful learning technique, that generally yields better results than an individual model, though it's no guarantee. Each model has a small factor of randomness, and increasing the number of models with their own quirks and traits, having them work together typically reduces the variance of the output and "irons out" irregularities.

Different realtors have seen different properties in their time, and each has their own wisdom. It's typical to hear a ballpark of appraisals from different people. In the illustration above, our orange client got a much more accurate idea of the value of the house just by averaging out the generally correct appraisals by multiple realtors, rather than listening to just one.

In a similar way, many psychological studies show how diversity in human teams helps find solutions to problems faster. Harvard Business Review outlies several studies that show how diverse teams display more accurate group thinking, process facts more carefully and display a higher level of innovation.

It's not hard to grasp why - having multiple sets of eyes and lenses on a subject makes it easier to see more details. Having a more diverse set of models predicting a target variable and working together to produce this result helps with accuracy.

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