A bank is interested in identifying different attributes of its customers, and below is the sample data of 150 customers. For Gender, 0 represents Male and 1 represents Female. For Personal loan, 0 represents a customer who has not taken a personal loan and 1 represents a customer who has taken a personal loan. Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. In XLMiner, fit a classification tree using Age, Gender, Work experience, Income (in $1000s), and Family size as input variables and Personal loan as the output variable. Be sure to Normalize input data and to set the Minimum #records in a terminal node to 1. Set the maximum number of levels to seven. Generate the Full tree, Best pruned tree, and Minimum error tree. Generate lift
charts for both the validation data and the test data.
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For the default cutoff value of 0.5, what is the overall error rate, Class 1 error rate, and Class 0 error rate of the best pruned tree on the test data? Interpret these respective measures.
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AgeGenderWork experienceIncome (in $1000s)Family sizePersonal loan470225331261322113801629413701232614402232305513045704412350203015222063035562034182340520262911550253421520284531631292331511303240410182151370144341460232331301618314812534205012221115603124403519233139013295148022346051121392127132611571324921331123931580333220460214531320623505612845313511228414712338115002332305712532413801525515212422215603119314712434405403145212502211140116346061130492129163411520253931560315421610334320260423216013056303711223503911439404612134505913039205403128112704221154030452042118364064035462033183261650343610380133240481232650311732303901535515812845205102623304101834303611232203901222405802934204211545104412132306513554204001323503311141404512429503801224413211028315103043213201134114602239414412125304111528215412954412613242033082631451203440631305420550314931491253451641355420260319104211923114812243316413445205202832404101634314001434504001626405513237214902739304602129105913256205102623314502343413911726415513237516303543205312724113901643615012556302704321129062321610304510571273421561345440351645502503322157027362047025544160135333032084521271432103901839412601221046125345028042320640374340651334920471253740271528212503341125022411640305320441214851650254720540315511510264330510214611281425215613255315712649213519276147121544154127452028152931450224550431184321?
What will be an ideal response?
For the default cutoff value of 0.5 on the best pruned tree on the test data, the overall error rate is 46.67%, the class 1 error rate is 61.90%, and the class 0 error rate is 11.11%. That is, the best pruned tree classifies a randomly-selected observation in the test data correctly 46.67% of the time. For a randomly-selected observation who has taken a personal loan, the best pruned tree will correctly classify it 61.90% of the time. For a randomly-selected observation who has not taken a personal loan, the best pruned tree will correctly classify it only 11.11% of the time.?
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