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The process of boosting involves improving the power of a machine learning program by adding more complex or capable algorithms. This process can reduce both bias and variance in machine learning, which helps to create more effective results.
Boosting processes are aimed at creating better overall machine learning programs that can produce more refined results. One way to look at this concept is in the context of weak and strong learning – where data scientists posit that a weak learner can be turned into a strong learner with either iteration or ensemble learning, or some other kind of technique. For example, stringing together several weaker algorithms can result in a stronger result.
Specific algorithms like AdaBoost or adaptive boosting use items like decision trees to creatively cobble together a stronger learning paradigm. That is the idea behind boosting, and it is something that is being used commonly in the evolution of machine learning technology.