Publications by Jordan Dever
Pred Modeling Final Project
fund = read.csv('fundraising.csv') future_fund = read.csv('future_fundraising.csv') EDA We need to take a look at the data set and see what the variables are head(fund) ## zipconvert2 zipconvert3 zipconvert4 zipconvert5 homeowner num_child income ## 1 Yes No No No Yes 1 1 ## 2 No...
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Assignment 8 SVMs
5. We have seen that we can ft an SVM with a non-linear kernel in order to perform classifcation 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. 5a) Generate a data set with n = 500 and p = 2, suc...
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Assignment 8 Placeholder
R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com. When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within...
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Assignment 8 Placeholder
Placeholder document until my code finishes running. Have been waiting 3 hours and still need 3 more SVMs to tune. title: “Assignment 8 SVMs” author: “Jordan Dever” date: “2024-04-23” output: html_document editor_options: markdown: wrap: 72 {r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) 5. We have seen that we can ft an ...
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Assignment 7
3. Consider the Gini index, classifcation error, and entropy in a simple classifcation setting with two classes. Create a single plot that displays each of these quantities # as a function of pˆm1. The x-axis should display pˆm1, ranging from 0 to 1, and the y-axis should # display the value of the Gini index, classifcation error, and entro...
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Assignment 1 Updated
2. Regression, Inference, n = 500 , p = 3 Classification, Prediction, n =20, p = 13 Regression, Prediction, n = 52, p = 3 5. A very flexible approach can be disadvantageous when the model overfits to the training data, then leads to a reduced Test MSE. It can be advantageous to not have a strict shape over the data so we can reduce the bias in...
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Assignment 2 Updated
2. Carefully explain the differences between the KNN classifier and KNN regression methods. One difference between KNN classifier and KNN regression is that in KNN classifier, is that KNN regression would be used in situations where our target variable is numeric / continuous where y is any number and we would use KNN classifier if our target i...
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Assignment 3 Updated
13. 13a) weekly = ISLR2::Weekly Loading data from ISLR2 package, then producing some numerical and graphical summaries of the data. summary(weekly) ## Year Lag1 Lag2 Lag3 ## Min. :1990 Min. :-18.1950 Min. :-18.1950 Min. :-18.1950 ## 1st Qu.:1995 1st Qu.: -1.1540 1st Qu.:...
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Predictive Modeling Assignment 5
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: Less flexible and hence will give improved prediction accuracy when its increase in bias is less than its decrease in variance. Lasso improves prediction accuracy and reduces variance by utilizing the pe...
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Predictive Modeling Assignment 4
#3. a) Explain how k-fold cross-validation is implemented K-fold CV is implemented by taking a data set and randomly partitioning it into ‘k’ number of equally sized folds. These folds allows us to create train/test splits on data sets that do not really offer that much data (<1000, or more if we are willing to use the computing power). The...
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