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Ensemble Machine Learning by Ankit Dixit

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Stacking

In the previous two methods, we got an idea of how multiple models of the same kind can help us improve the accuracy of our classification. What if we combine models of two different kinds? Can it help us? The answer is yes! In some cases, it is very helpful to use different kinds of prediction models to get higher prediction accuracy. Well, the next question is: how?

As we have seen, a single decision tree in a weak learner (boosting) or in bagging can help us only to make partial predictions. To reduce the bias error from our model, we need to increase the number of classifiers in our ensemble framework. In the same way, for very complex datasets, a single solution might not give us a higher prediction rate. For such situations ...

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