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- University
- Politecnico di Milano
- Degree programme
- Computer Engineering
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- Data Mining and Text Mining
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- Exam · Full 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 Prof. Pier Luca Lanzi & Ing. Daniele Loiacono July 14, 2016 NAME CODICE PERSONA/ID Answers must be clearly written inside the problem box. All the answers must be adequately
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 Prof. Pier Luca Lanzi & Ing. Daniele Loiacono July 14, 2016 NAME CODICE PERSONA/ID Answers must be clearly written inside the problem box. All the answers must be adequately
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Politecnico di Milano School of Industrial and Information Engineering Data Mining and Text Mining Prof. Pier Luca Lanzi & Ing. Daniele Loiacono July 14, 2016 NAME CODICE PERSONA/ID Answers must be clearly written inside the problem box. All the answers must be adequately motivated. Pencils are not allowed. The midterm consists of 4 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. Scoring o A problem left unsolved will amount to zero points. o A completely wrong solution will amount to -3 points Grades Problem 1. (7 points) Consider the task of building a classifier from random data, where the attribute values are generated randomly irrespective of the class labels. A ssume the data set contains records from two classes, “+” and “−.” Half of the data set is used for training while the remaining half is used for testing. Suppose there are an equal number of positive and negative records in the data and the decision tre e classifier predicts every test record to be positive. What is the expected error rate of the classifier on the test data? Repeat the previous analysis assuming that the classifier predicts each test record to be positive class with probability 0.8 and negative class with probability 0.2. Suppose two -thirds of the data belong to the positive class and the remaining one -third belong to the negative class. What is the expected error of a classifier that…
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