Suppose we wish to train a classification tree to predict whether a new smartphone will be profitable on Amazon com To build the classification tree, we have a dataset which consists of hundreds of smartphones that have been sold in the past year A sample of the rows of the dataset is shown below Product ID Large screen ( 5in) Expensive ( $600) Long battery life Profitable x8395wkk33 Yes No Yes Yes 552kx52jlk1 No No Yes Yes 35q593jpop2 Yes Yes No No Suppose that we trained a classification tree to predict the column Profitable using the other four columns as the independent variables If we constructed the tree using the information gain approach, would you expect the resulting tree to overfit the data Why or why not

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Question: Suppose we wish to train a classification tree to predict whether a new smartphone will be profitable on Amazon.com. To build the classification tree, we

Suppose we wish to train a classification tree to predict whether a new smartphone will be profitable on Amazon.com. To build the classification tree, we have a dataset which consists of hundreds of smartphones that have been sold in the past year. A sample of the rows of the dataset is shown below:

Product ID	Large screen (>= 5in)	Expensive (>= $600)	Long battery life	Profitable
x8395wkk33	Yes	No	Yes	Yes
552kx52jlk1	No	No	Yes	Yes
35q593jpop2	Yes	Yes	No	No
....	...	...	...	...

Suppose that we trained a classification tree to predict the column "Profitable" using the other four columns as the independent variables. If we constructed the tree using the information gain approach, would you expect the resulting tree to overfit the data? Why or why not?

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