Date of Award:

8-2023

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Mathematics and Statistics

Committee Chair(s)

Brennan Bean

Committee

Brennan Bean

Committee

Stephen Walsh

Committee

Brent Thomas

Abstract

Many discipline specific researchers need a way to quickly compare the accuracy of their predictive models to other alternatives. However, many of these researchers are not experienced with multiple programming languages. Python has recently been the leader in machine learning functionality, which includes the PyCaret library that allows users to develop high-performing machine learning models with only a few lines of code. The goal of the stressor package is to help users of the R programming language access the advantages of PyCaret without having to learn Python. This allows the user to leverage R’s powerful data analysis workflows, while simultaneously leveraging Python’s powerful machine learning functionality. stressor also implements a series of synthetic data set generation functions that create data sets where users can test ideas with models they create and/or use. These data sets can be paired with various forms of accuracy comparison to stress-test the models predictive capacity. This thesis illustrates this stress-test workflow on both real and synthetic data, illustrating stressor’s utility and ease of use.

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