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- Politecnico di Milano
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- Computer Engineering
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- Data Mining and Text Mining
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- Exam · Full exam
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2015 07 08
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 July 8, 2015 NAME MATRICOLA Solve the following problems and write the answer inside the problem box.
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 July 8, 2015 NAME MATRICOLA Solve the following problems and write the answer inside the problem box.
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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 July 8, 2015 NAME MATRICOLA Solve the following problems and write the answer inside the problem box. Answers must be clearly written. Pencils are not allowed. The midterm consists of 3 sheets of paper. It must be returned with all the 3 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. All the answers must be adequately motivated. Grades 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 dataset below, where the class is represented by the attribute Edible with values Yes and No. Apply the OneRule algorithm to this dataset. Note that the attribute Odor should be treated as a nominal. Shape Color Odor Edible C B 1 Yes D B 1 Yes D W 1 Yes D W 2 Yes C B 2 Yes D B 2 No D G 2 No C U 2 No C B 3 No C W 3 No D W 3 No Problem 1. (continued) Problem 2. Impute the missing values in the following data set using the most adequate techniques among the ones discussed during the course. Discuss the underlying assumptions (if any) you are making to perform imputation in this scenario. Outlook Temp Humidity Windy Play Sunny 85 85 False No Sunny 80 90…
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