Publications by Devin Quebodeaux
HW6CH7DevinQuebodeaux
6. In this exercise, you will further analyze the Wage data set considered throughout this chapter. library("ISLR") ## Warning: package 'ISLR' was built under R version 4.0.5 attach(Wage) ?Wage ## starting httpd help server ... done wages<-Wage library(boot) (a) Perform polynomial regression to predict wage using age. Use cross-validation to sel...
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HW4Ch5DevinQuebodeaux 3,5,6,9
R Markdown 3. We now review k-fold cross-validation. (a) Explain how k-fold cross-validation is implemented. K-fold is dividing sets of observation randomly into K groups.All groups will equal in size and non are overlapped. These are considered validation sets. (b) What are the advantages and disadvantages o fk-fold cross-validation relative to:...
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ISLR Chapter 4. Problems 10,11,13
R Markdown 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 of2010. library(ISLR) ## Warning: package 'ISLR' was built und...
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R Markdown 2. Carefully explain the differences between the KNN classifier and KNN regression methods. KNN classifier is used for classification problems where KNN regression is used for regression problems. Classifier 0 to 1 Regression the quantitative value of Y. library(MASS) library(ISLR) ## Warning: package 'ISLR' was built under R version ...
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HW7Ch8DevinQuebodeaux
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, classification error, and entropy. p...
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HW8Ch9DevinQuebodeaux
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. library(caret) ## Warning: package 'caret' was built und...
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