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This paper is based on an earlier project by Curana and Niculescu-Mizil. They iterate through eight algorithms and datasets to compare the relative abilities of each algorithm. Their paper guides users to choose algorithms and consider what the different classifiers output: probabilities, class labels, etc. These are compared across different binary classification datasets and different metrics. For the individual version of this project, only three classification algorithms, four datasets, and three metric measurements are required. Caruana gives the groundwork of how to format the information coming from the algorithms to render them comparable. Statistical analysis is used to further represent the merits of this approach.
Individual COGS 108 Projects (Fa20)
Lecture Files - Fall 2020
To be used for the assignments in Cogs 108
Text Analytics Rools
Final Project materials and description.
Discussion Section Materials (Fa20)
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