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2013 09 11

Full exam for Data Mining and Text Mining in the Computer Engineering degree programme at Politecnico di Milano. The document covers: Politecnico di Milano Facoltà di Ingegneria dell’Informazione Data Mining and Text Mining Tecniche di Apprendimento Automatico Prof. Pier Luca Lanzi & Ing. Daniele Loiacono September 11, 2013 NAME MATRICOLA Solve the following problems and write the answer inside the problem

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 Facoltà di Ingegneria dell’Informazione Data Mining and Text Mining Tecniche di Apprendimento Automatico Prof. Pier Luca Lanzi & Ing. Daniele Loiacono September 11, 2013 NAME MATRICOLA Solve the following problems and write the answer inside the problem

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Politecnico di Milano Facoltà di Ingegneria dell’Informazione Data Mining and Text Mining Tecniche di Apprendimento Automatico Prof. Pier Luca Lanzi & Ing. Daniele Loiacono September 11, 2013 NAME MATRICOLA Solve the following problems and write the answer inside the problem box. Answers must be clearly written. Pencils are not allowed. The final consists of 5 sheets of paper. It must be returned with all the 5 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. Grades Data Mining and Text Mining Problems 1, 2, 3, 4 and 5 Tecniche di Apprendimento Automatico per Applicazioni di Data Mining Problems 1, 2, 3, 4 and 5 The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again. Problem 1. Consider the decision tree below in which for each node the number of examples of class 0 are reported on the left and the number of examples of class 1 are reported on the right. Compute the impurity of nodes t1, t2 and t3 using the Gini index; compute the impurity reduction achieved by the first split (the one in the root node). Problem 2. Suppose you are applying bagging trees using 25 base classifiers. Each classifier has error rate, e =0.35 and assume classifiers are independent. Compute the probability that the ensemble classifier makes a wrong…

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