Classification and Analysis of Political Video Advertisements Stumbaugh, Scott
dc.contributor.committeeMember Sabzikar, Farzad
dc.contributor.majorProfessor Nordman, Daniel
dc.contributor.majorProfessor Vardeman, Stephen 2022-06-06T16:31:55Z 2022-06-06T16:31:55Z 2021 2021-12
dc.description.abstract As the ever-increasing fanfare around machine learning grows, so do the seemingly endless number of methods. Without a good grasp of the statistical fundamentals, there is a tendency to think this class of models is a black box that functions as the “magic bullet,” able to blindly solve data problems in every situation. Taking time to examine the underlying principles, and break down how they steer certain model behaviors in the presence of a particular data set, helps to understand how a model will perform and why. Motivated by this mind set, the analytical approach of this paper aims to explore a novel modification to the classic decision tree model that incorporates a two-step-look-ahead approach. Included is a comparative analysis between the original algorithm (one-step-look-ahead logic) and the new one, which shows in the case of a particular simulated dataset that the two-step-look-ahead logic vastly outperforms the classic decision tree algorithm.
dc.language.iso en
dc.rights.holder Scott Stumbaugh
dc.subject.disciplines DegreeDisciplines::Physical Sciences and Mathematics::Statistics and Probability
dc.subject.keywords Decision Tree
dc.subject.keywords Custom Tree
dc.subject.keywords Dual Stage Tree
dc.subject.keywords Double Splitting Tree
dc.subject.keywords Two-Stage Tree
dc.subject.keywords Two-Step Tree
dc.subject.keywords Double Binary Tree
dc.subject.keywords Hybrid Tree
dc.subject.keywords Two-Step-Look-Ahead-Tree
dc.title Classification and Analysis of Political Video Advertisements
dc.type Text
dc.type.genre creativecomponent
dspace.entity.type Publication
relation.isDegreeOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca Statistics Statistics Masters Master of Arts/Master of Science
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