Publications by George Shibley
George Shibley sfj479 Assignment2
Question # 2: Carefully explain the differences between the KNN classifier and KNN regression methods. It really comes down to the outcome you are trying to predict (a numerical vs categorical response). With KNN, these are non-parametric approaches that both use some form of local approximation to predict an outcome. The different outcome refl...
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George Shibley sfj479 Assignment 5
2. For parts (a) through (c), indicate which of i. through iv. is correct. Justify your answer. More flexible and hence will give improved prediction accuracy when its increase in bias is less than its decrease in variance. More flexible and hence will give improved prediction accuracy when its increase in variance is less than its decrease in...
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George Shibley sfj479 Assignment 4
3. We now review k-fold cross-validation. (a) Explain how k-fold cross-validation is implemented. With k-fold Cross Validation you first divide the observations into k different non-overlapping groups of apx equal size. You then remove the first group, fit the model on the remaining k-1 groups, and see how good the predictions are on the left out...
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George Shibley sfj479 Assignment 3
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. library(ISLR) library(MASS) data("Weekly") Produce some numeri...
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George Shibley sfj479 Assignment 6
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 plot of ...
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George Shibley sfj479 Assignment 7
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 ˆ pm1. The x axis should display ˆ pm1, ranging from 0 to 1, and the y-axis should display the value of the Gini index, classification error, and entro...
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DocumeGeorge Shibley sfj479 Assignment 7nt
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, such t...
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