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Undecided Voters ( kNN classification). Campaign organizers for both the Republican and Democratic parties are interested in identifying individual undecided voters who would consider voting for their party in an upcoming election. A non-partisan group has collected data on a sample of voters with tracked variables, including whether or not they are undecided regarding their candidate preference, age, whether they own a home, gender, marital status, household size, income, years of education, and whether they attend church. Using Undecided as the target (or response) variable, construct a series of k-nearest neighbor classifiers as directed by the following parts (a), (b), and (c). Evaluate a range of values of k and standardize the input variables to adjust for the different magnitudes of the variables. a. Use only the continuous variables (Age, HouseholdSize, Income, and Education) as input variables. For a default cutoff value of 0.5 , what value of k minimizes the overall error rate on a static validation set or through a 10 -fold cross-validation procedure? b. Use all eight variables as input variables. For a default cutoff value of 0.5 , what value of k minimizes the overall rate on a static validation set or through a 10 -fold cross-validation procedure? c. Generally, caution is recommended when combining continuous input variables and categorical input variables as the concept of distance differs for these two types of variables. Compare the overall error rates of models from parts (a) and (b). For these data, does combining variable types degrade performance