Abstract

Chess is a two-player game, popular with a wide variety of people, ranging from people who play casually every once in a while with their family to professional players who make their living through playing and teaching. There are three outcomes that a chess game could have: player 1 (White) wins, player 2 (Black) wins, or both players draw. Using an online database called “The Week In Chess” which contains information about 2.5 million chess games, a variety of machine learning methods are tested to predict the outcomes of chess games. The features investigated as inputs for classification are: White’s Win Rate, White’s Draw Rate, Black’s Win Rate, Black’s’ Draw Rate, Opening’s Win Rate and Opening’s Draw Rate. Then, the results of the classifiers using the full set of features and various subsets of features are compared in order to try and determine the best possible accuracy in classifying the game outcome.

Publication Date

6-7-2024

Document Type

Thesis

Student Type

Graduate

Degree Name

Applied and Computational Mathematics (MS)

College

College of Science

Advisor

Nathan Cahill

Advisor/Committee Member

Matthew Coppenbarger

Advisor/Committee Member

Ernest Fokoue

Campus

RIT – Main Campus

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