Apply a Bagging model that consists of 10 base decision trees for classifying the events as normal or anomalous. Fill in the myBagging function, which accepts as input the training set and returns a fully trained model. Below is the template def myBagging(Xtrain, ytrain):   #write function here   return myBagging

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Decision Tree

A decision tree is a unique kind of probability tree. A popular and powerful tool used for prediction and classification. The decision tree structure is the same as a flowchart with a tree structure. The internal node refers to the attribute test, the branch refers to the outcome of the test, and the leaf node(terminal) contains the class label.