Print Email Facebook Twitter Learning Curve Extrapolation using Machine Learning Title Learning Curve Extrapolation using Machine Learning: Benefits and Limitations of using LCPFN for Learning Curve Extrapolation Author Johari, Pratham (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Viering, T.J. (mentor) Turan, O.T. (mentor) Hung, H.S. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2024-02-01 Abstract This study explores the extrapolation of learning curves, a crucial aspect in evaluating learner performance with varying dataset sample sizes. We use the Learning Curve Prior Fitted Network (LC-PFN), a transformer pre-trained on synthetic data with proficiency in approximate Bayesian inference, to investigate its predictive accuracy using the Learning Curve Database (LCDB). The assessment involves MSE as an error metric, with 2 baselines from previous studies where we see it outperform the baseline in some cases and keep on par in others. Additionally, we scrutinize instances where the LC-PFN model may exhibit shortcomings to identify trends in curve extrapolation failures, offering insights for potential modifications to the training dataset. We see a pattern in learners where LC-PFN performs consistently poorly on, whereas no significant pattern can be seen for datasets. Subject Learning curveExtrapolationLCDBLCPFNMachine learning To reference this document use: http://resolver.tudelft.nl/uuid:a2c5e474-cd2d-4096-bc2d-5ef2ac76803f Part of collection Student theses Document type bachelor thesis Rights © 2024 Pratham Johari Files PDF pratham_v_1_0_5.pdf 4.35 MB Close viewer /islandora/object/uuid:a2c5e474-cd2d-4096-bc2d-5ef2ac76803f/datastream/OBJ/view