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As the use of data mining and machine learning methods in the humanities becomes more common, it will be increasingly important to examine implicit biases, assumptions, and limitations these methods bring with them. This paper makes explicit some of the foundational assumptions of machine learning methods, and presents a series of experiments as a case study and object lesson in the potential pitfalls in the use of data mining methods for hypothesis testing in literary scholarship.
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