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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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2014 05 14
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 May 14, 2014 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 May 14, 2014 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 May 14, 2014 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. Compute the best splitting attribute among Age, Spectacle prescription, and Astigmatism using the information gain for the following data set in which attribute Recommended lenses is the class. Suppose that we also apply the OneRule algorithm next. What is the relation between the best splitting attribute selected in the previous step and the attribute selected by the OneRule algorithm? None Reduced Yes Hypermetrope Pre-presbyopic None Normal Yes Hypermetrope Pre-presbyopic None Reduced No Myope Presbyopic None Normal No Myope Presbyopic None Reduced Yes Myope Presbyopic Hard Normal Yes Myope Presbyopic None Reduced No Hypermetrope Presbyopic Soft Normal No…
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