This project builds machine learning models that try to predict whether a claim can be supported, refuted, or there is insufficient information to determine this based on existing and new predictors in the FEVER (Fact Extraction and VERification) dataset. The models compared are logistic regression with LASSO regularization, K-Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest.
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My final project for DACSS 695M (Machine Learning for the Social Sciences) titled 'Automating Fact-Checking using Machine Learning Models with the FEVER Dataset'
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