wk02 - CS195f Homework 1: Naive Bayes Mark Johnson and Erik...

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CS195f Homework 1: Naive Bayes Mark Johnson and Erik Sudderth Homework due at 2pm, 24th September 2009 The Nursery database records a series of admission decisions to a nursery in Ljubljana, Slovenia. We downloaded this data from http://archive.ics.uci.edu/ml/datasets/Nursery , which you can see for more details if you’re interested. The database contains one tuple for each admission decision. The features or attributes include financial status of the parents, the number of other children in the house, etc. The first three tuples in the dataset are as follows: usual,proper,complete,1,convenient,convenient,nonprob,recommended,recommend usual,proper,complete,1,convenient,convenient,nonprob,priority,priority usual,proper,complete,1,convenient,convenient,nonprob,not_recom,not_recom where the first 8 values are features or attributes and the 9th value is the class assigned (i.e., the admission decision recommendation). Your job is to build a Naive Bayes classifier that will make admission recommendations. Luckily the really hard work of data preparation has been done for you by Deqing, our fearless TA. The file /course/cs195f/asgn/naive_bayes/handout/nursery/nursery.mat contains this in a matrix format that Matlab can directly read. All of the symbols have been replaced with identifying integers. The first three rows of this matrix are: >> load(’/course/cs195f/asgn/naive_bayes/handout/nursery/nursery.mat’); >> data(1:3,:) ans = 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 4 1 1 1 1 1 1 1 3 1 You should divide this data into equal-sized training and testing data sets as follows (the
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wk02 - CS195f Homework 1: Naive Bayes Mark Johnson and Erik...

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