中科院数据挖掘作业2

HW2Due Date: Nov. 23Submission requirements:Please submit your solutions to our class website. Only hand in what is required below.Part I: written assignment1.a) Compute the Information Gain for Gender, Car Type and Shirt Size.b) Construct a decision tree with Information Gain.2. (a) Design a multilayer feed-forward neural network (one hidden layer) for thedata set in Q1. Label the nodes in the input and output layers.(b) Using the neural network obtained above, show the weight values after oneitera tion of the back propagation algorithm, given the training instance “(M,Family, Small)". Indicate your initial weight values and biases and the learning rate used.3.a) Suppose the fraction of undergraduate students who smoke is 15% and thefraction of graduate students who smoke is 23%. If one-fifth of the college students are graduate students and the rest are undergraduates, what is the probability that a student who smokes is a graduate student?b) Given the information in part (a), is a randomly chosen college student more likelyto be a graduate or undergraduate student?c) Suppose 30% of the graduate students live in a dorm but only 10% of theundergraduate students live in a dorm. If a student smokes and lives in the dorm, is he or she more likely to be a graduate or undergraduate student? You can assume independence between students who live in a dorm and those who smoke.4. Suppose that the data mining task is to cluster the following ten points (with(x, y, z) representing location) into three clusters:A1(4,2,5), A2(10,5,2), A3(5,8,7), B1(1,1,1), B2(2,3,2), B3(3,6,9), C1(11,9,2),C2(1,4,6),C3(9,1,7), C4(5,6,7)The distance function is Euclidean distance. Suppose initially we assign A1,B1,C1 as the center of each cluster, respectively. Use the K-Means algorithm to show only(a) The three cluster center after the first round execution(b) The final three clustersPart II: LabQuestion 1Assume this supermarket would like to promote milk. Use the data in “transactions” as training data to build a decision tree (C5.0 algorithm) model to predict whether the customer would buy milk or not.1. Build a decision tree using data set “transaction s” that predicts milk as a function of theother fields. Set the “type” of each field to “Flag”, set the “direction” of “milk” as “out”, set the “type” of COD as “Typeless”, select “Expert” and set the “pruning severity” to 65, and set the “minimum records per child branch” to be 95. Hand-in: A figure showing your tree.2. Use the model (the full tree generated by Clementine in step 1 above) to make apredic tion for each of the 20 customers in the “rollout” data to determine whether the customer would buy milk. Hand-in: your prediction for each of the 20 customers.3. Hand-in: rules for positive (yes) prediction of milk purchase identified from the decisiontree (up to the fifth level. The root is considered as level 1). Compare with the rules generated by Apriori in Homework 1, and submit your brief comments on the rules (e.g., pruning effect)Question 2: Churn ManagementThe goal of this assignment is to introduce churn management using decision trees, logistic regression and neural network. You will try different combinations of the parameters to see their impacts on the accuracy of your models for this specific data set. This data set contains summarized data records for each customer for a phone company. Our goal is to build a model so that this company can predict potential churners.Two data sets are available, churn_training.txt and churn_validation.txt. Each data set has 21 variables. They are:State:Account_length: how long this person has been in this planArea_code:Phone_number:International_plan: this person has international plan=1, otherwise=0Voice_mail_plan: this person has voice mail plan=1, otherwise=0Number_vmail_messages: number of voice mailsTotal_day_minutes:Total_day_calls:Total_day_charge:Total_eve_minutes:Total_eve_calls:Total_eve_charge:Total_night_minutes:Total_night_calls:Total_night_charge:Total_intl_minutes:Total_intl_calls:Total_intl_charge:Number_customer_service_calls:Class: churn=1, did not churn=0Each row in “churn_training” represents the customer record. The training data contains 2000 rows and the validation data contains 1033 records.1. Perform decision tree classification on training data set. Select all the input variablesexcept state, area_code, and phone_number (since they are only informative for this ana lysis). Set the “Direction” of class as “out”, “type” as “Flag”. Then, specify the “minimum records per child branch”as 40, “pruning severity” as 70, click “use global pruning”. Hand-in the confusion matrices for validation data.2. Perform neural network on training data set using default settings. Again, select all the input variables except state, area_code, and phone_number. Hand-in the confusion matrix for validation data.3. Perform logistic regression on training data set using default settings. Again, select all the input variables except state, area_code, and phone_number. Hand-in the confusion matrix for validation data.4. Hand-in your observations on the model quality for decision tree, neural network and logistic regression using the confusion matrices.。