What is occam razor principle

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Reference no: EM132974658

Reference: Predictive Analytics book by eric seigel.chapters 4 & 5. Refer chapter 4 and 5 of the above mentioned book and answer the questions in detail.

Please answer the following questions in detail

a. Does lift increase by increasing the number of segments in a tree? What is the maximum number of segments possible for a dataset with n training samples? Do you expect a high or a low lift from such a big decision tree?

b. Based on the 'no free lunch' theorem, which is the best machine learning method?

c. What is common practice for testing a learning method for overlearning? Does this method also eliminate requirements to explicitly account for 'vast search' (as in the 'orange lemon' case)?

d. What is Occam's razor principle? How is it relevant to overlearning?

e. Explain the ensemble model. What is meta-learning? How did this model help the winner of the Netflix Prize?

Reference no: EM132974658

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