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In several practical analytics scenarios we need to perform machine learning with specific output variable. In the case of examples like determining if a person has to be given a credit card or not the output, variable is discrete and the method is classification.
However to carry out the classification we do not apply the machine learning algorithm directly ..instead we try to look at the process of getting a model by combining techniques for combining several individual machine learning models.. example is bagging tries to get several hundreds of models and then the final result is obtained as aggregation of the smaller models..Random forest is formed by bagging of several decision trees..
In other examples we use techniques of stacking and boosting to combine several individual machine learning models into an ensemble..these ensembles are also called meta algorithms

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