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# Machine Learning

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Text classification is a core machine learning technique that can help you label your texts. Text analysis (TA) is a machine learning technique used to automatically extract valuable insights from unstructured text data.
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Test Pearl 100  Intelligent Interaction October 2 2015 The test consists of 5 questions. The grade is the number of achieved points divided 100. 1 (20 points) A bagH 1 contains 10marbles: 2 red, 3 white and 5 blue. Bag H 2 contains also 10marbles: + 4 red, 2 white and 4 blue. Someone throws a fair dice and if the outcome is divisible by 3 then he chooses bag H 1, else he chooses bag H 2. After choosing a bag he draws 5 marbles with replacement. The outcome D is 2 red, 2 white and 1 blue marble, so D=< 2;2 ;1 > . (a)Compute P(D jH 1) . (b)What is the most likely bag from which the marbles are drawn; H 1or H 2? Motivate your answer by a computation using Bayes law. Antwoord op 1. 1.P(D jH 1) = 5! 2!1!2! (2 10 )2 ( 3 10 )2 ( 5 10 ) = 0:054 2. P(D jH 2) = 5! 2!1!2! (4 10 )2 ( 2 10 )2 ( 4 10 ) = 0:0768. And P(H 1j D )= P (H 2j D ) equals [( 3 10 )2 ( 5 10 )( 1 3 )] =[( 4 10 )2 ( 4 10 )( 2 3 )] = 45=128 =0:35. Hence H 2is the most likely bag. 2 (20 points) Given the following piece of text from an email: + attention if you are in debt. if you are then we can help. qualifying is now at your ngertips and there are no long distance calls (a)Assume that we use as vocabulary V=fattention, adult, debt, publications, qualifying, xxx g. How would this piece of text be coded using a binary coding and this vocabulary V? (b)For convenience consider a smaller vocabulary V=fattention, adult, debt gand assume that we have a dataset consisting of 100 emails of which 30 are spam and with the following vocabulary frequency list: Word Ham Spam attention 30 10 adult 0 22 debt 4 20 This means for instance that the word attention occurs in 30 ham emails and in 10 spam emails. Assume that a new email arrives with binary coding <1>. Compute the likelihood that this email is from the spam class. in other words compute P(< 1;0 ;1 > jSpam ). (c)How i
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