Publications by Richard Rodriguez-ccb680
STA-6543 Assignment 7 - Rodriguez-ccb680
Chapter 08 - Tree-Based Methods (page 332): 3, 8, 9 Exercise 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 xaxis should display ˆpm1, ranging from 0 to 1, and the y-axis should display the v...
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Assignment 6 - Rodriguez-ccb680
Chapter 07 - Moving Beyond Linearity (page 297): 6, 10 Exercise 6. In this exercise, you will further analyze the Wage data set considered throughout this chapter. Ex. 6a.) 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 comp...
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STA-6543-01T-Summer Algorithms II - Assignment 5 - Rodriguez-ccb680
Chapter 06 (page 259): 2, 9, 11 Exercise 2. For parts (a) through (c), indicate which of i. through iv. is correct. Justify your answer. The lasso, relative to least squares, is: 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 impro...
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Assignment-4
Chapter 05 (page 197): 3, 5, 6, 9 Exercise 3. We now review k-fold cross-validation. (3a.) Explain how k-fold cross-validation is implemented. 3a. Answer: Section 5.1.3 k-Fold Cross Validation: This approach involves randomly dividing the set of observations into k groups, or folds, of approximately equal size. The first fold is treated as a vali...
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Exercise3 - Rodriguez-ccb680
R Markdown 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 and graphical summaries of the We...
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STA-6543 Assignment 2 - Rodriguez - ccb680
Chapter 03 (page 120): 2, 9, 10, 12 2.) Carefully explain the differences between the KNN classifier and KNN regression methods. 2.) Answer KNN classifier assigns classification group based on majority of closest observations while the KNN regression averages the closest observations to an estimated prediction. 9.) This question involves the us...
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STA-6543 Assignment 8 - Rodriguez-ccb680
Chapter 09 - Support Vector Machines (page 332): 5, 7, 8 Exercise 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 ...
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