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Full exam for Data Mining and Text Mining in the Computer Engineering degree programme at Politecnico di Milano. The document covers: Politecnico di Milano School of Industrial and Information Engineering Data Mining and Text Mining July 7, 2018 FAMILY NAME FIRST NAME CODICE PERSONA/ID GENERAL INSTRUCTIONS • Answers must be clearly written inside the answer box designated for each. All the answers must be

Data Mining and Text MiningFull exam

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Full exam for Data Mining and Text Mining in the Computer Engineering degree programme at Politecnico di Milano. The document covers: Politecnico di Milano School of Industrial and Information Engineering Data Mining and Text Mining July 7, 2018 FAMILY NAME FIRST NAME CODICE PERSONA/ID GENERAL INSTRUCTIONS • Answers must be clearly written inside the answer box designated for each. All the answers must be

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Politecnico di Milano School of Industrial and Information Engineering Data Mining and Text Mining July 7, 2018 FAMILY NAME FIRST NAME CODICE PERSONA/ID GENERAL INSTRUCTIONS • Answers must be clearly written inside the answer box designated for each. All the answers must be adequately motivated. • Pencils are not allowed. The exam consists of 6 sheets of paper. It must be returned with all the 4 sheets. No any other sheet can be added. No sheet can be removed. • This is a closed-book/closed-notes exam. • Only non-programmable calculators are allowed. • Notes/books/mobile phones are not allowed. • If you are caught using forbidden material, the exam will immediately end and an RP grade will be recorded; then, your Data Mining exam will consist of an oral examination from then on. SCORING • A problem left unsolved will amount to zero points. • A completely wrong solution will amount to -3 points STUDENTS WHO DID THE COURSE PROJECT HAVE 1:40h TO SOLVE PROBLEMS 1, 2, 3, AND 4 ALL THE OTHER STUDENTS HAVE 2:20h TO SOLVE ALL THE SIX PROBLEMS COURSE PROJECT SCORE FINAL TIME GRADES 1 2 3 4 5 6 Problem 1 (6 points). (1) Write the pseudo code for building a random forest for classification from the data D and the number of models k, and a percentage F of features to be used during the building phase. The code must return a vector of models T where T[i] is the i-th model. (2) Write the pseudo code for computing the class predicted of a random forest model from a vector of k models T and an example d. (3) Explain how the out of bag evaluation of d should work. Notes a) Write the pseudo code in the corresponding boxes one instruction per line. b) Use pseudo code. Python is not required but feel free to use it if you want. c) Write the answer of question 3 in the corresponding box.…

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