Publications by Madison Dorsey
Assignment #4
Problem 3 We now reviewed k-fold cross validation. (a) Explain how k-fold cross-validation is implemented. k-fold cross validation is implemented by taking the set of n observations and randomly splitting into k non-overlapping groups. Each of these groups acts as a validation set and the remainder as a training set. The test error is estimated b...
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Problem 10 This question should be answered using the Weekly data set, which is part of the ISLR package. This data is similar in nature to the Smarket data from this chapter’s lab, except that it contains 1, 089 weekly returns for 21 years, from the beginning of 1990 to the end of 2010. (a) Produce some numerical and graphical summaries of the...
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STA 4143-001 Assignment #2
Question 2 Carefully explain the differences between the KNN classifier and KNN regression methods. They are quite similar. Given a value for K and a prediction point \(x_{0}\), KNN regression first identifies the K training observations that are closes to \(x_{0}\), represented by \(N_{i}\). It then estimates \(f(x_{0})\) using the average of al...
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Assignment #5
Problem 6 In this exercise, you will further analyze the Wage data set considered throughout this chapter. (a) Perform polynomial regression to predict wage using age. Use cross-validation to select the optimal degree d for the polynomial. What degree was chosen, and how does this compare to the results of hypothesis testing using ANOVA? Make a p...
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Assignment #6
Problem 3 Consider the Gini index, classification error, and entropy in a simple classification setting with two classes. Create a single plot that displays each of these quantities as a function of \(\hat{p}_{m1}\). The x-axis should display \(\hat{p}_{m1}\), ranging from 0 to 1, and the y-axis should display the value of the Gini index, classif...
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Assignment #7
Problem 5 We have seen that we can fit an SVM with a non-linear kernel in order to perform classification using a non-linear decision boundary. We will now see that we can also obtain a non-linear decision boundary by performing logistic regression using non-linear transformations of the features. (a) Generate a data set with n = 500 and p = 2, s...
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