Publications by Patti Geppert
Assignment 7
Assignment 7 Chapter 8: 3, 8, 9 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 ˆpm1. The x-axis should display ˆpm1, ranging from 0 to 1, and the y-axis should display the value of the Gini index, cla...
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Assignment 5
Assignment 5 Chapter 6: 2,9,11 2. For parts (a) through (c), indicate which of i. through iv. is correct. Justify your answer. (a) The lasso, relative to least squares, is: i. More flexible and hence will give improved prediction accuracy when its increase in bias is less than its decrease in variance. ii. More flexible and hence will give impro...
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Assignment 4
Assignment 4 Chapter 5: 3,5,6,9 3. We now review k-fold cross-validation. (a) Explain how k-fold cross-validation is implemented. K-fold cross-validation is implemented by selecting a value of K (Studies have shown that K = 5 and K=10 work well with reasonable bias and variance). The dataset is randomly divided into K equal subsets. For the K-f...
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Assignment 2
Assignment 2- Chapter 3: 2, 9, 10, 12 Carefully explain the differences between the KNN classifier and KNN regression methods. KNN classifier and KNN regression are closely related non-parametric methods. The KNN classifier classifies a new value based on the values of surrounding neighbors. The K value determines how many neighbors are used to...
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Assignment 3
Assignment 3: Chapter 4: 10, 11, 13 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. Produce some numerical an...
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Assignment 6
Assignment 6 Chapter 7: 6, 10 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 testi...
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Assignment 8
Assignment 8 Chapter 9: 5, 7, 8 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...
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