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Ensemble Machine Learning by Ankit Dixit

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Boosting

Unity is the power. Yes, the same concept can work in machine learning problems also. How? Oh yes it can, and the approach is known as boosting. It is a process in which we train multiple weak classifiers and combine their results to create a strong classifier. In theory, boosting algorithms are primarily used to prevent underfitting (high bias) and, of course, overfitting (high variance) of the classification model.  

There are many boosting algorithms used by the data science community, and in future chapters, we will discuss some of them in detail. These algorithms are AdaBoost, XGBoost, gradient boosting machines, and so on.

So how does boosting work? Well, you can see in Figure 1.4. It starts with bootstrapping of data, which ...

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