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cs.LG2026
When Offline Selectors Cannot Beat the Best Single Model: A Diagnostic Study on edX Dropout Prediction
Tyler Crosse, Alan Nadelsticher Ruvalcaba, Dustin Khang LeDuc +3
Different predictors often excel on different inputs, so picking the best one per instance promises higher accuracy than committing to a single model. In practice, selectors traine…
cs.LG2023
Analyzing the Capabilities of Nature-inspired Feature Selection Algorithms in Predicting Student Performance
Thomas Trask
Predicting student performance is key in leveraging effective pre-failure interventions for at-risk students. As educational data grows larger, more effective means of analyzing st…