Print Email Facebook Twitter In Search of Best Learning Curve Model Title In Search of Best Learning Curve Model Author Nguyen, Dean (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Viering, T.J. (mentor) Loog, M. (mentor) Smaragdakis, G. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-06-23 Abstract Learning curves have been used extensively to analyse learners' behaviour and practical tasks such as model selection, speeding up training and tuning models. Nonetheless, we still have a relatively limited understanding of the behaviour of learning curves themselves, in particular, whether there exists a parametric function that can best model all learning curves. Therefore, this study aims to determine which parametric models proposed over the years provide the best fit when applied to empirical learning curves. To answer this question, the study focuses on supervised learning and is divided into two parts: classification and regression tasks, and the learning curve data for each task was fitted using the Levenberg-Marquardt algorithm. Subsequently, the fitted models were analysed using the Friedman test, the Wilcoxon signed-rank test, and other metrics. The results indicate that a power law applies in most cases. However, a universal model has not been found, as the best model differs between classification and regression tasks, even though they belong to the power law family. Moreover, there are some deviations from these aggregate results when examining the learners individually, suggesting that a more granular approach is better suited for practical applications. Subject machine learninglearning curveclassificationregression To reference this document use: http://resolver.tudelft.nl/uuid:f6a51608-5186-4a33-acba-dbe73685a4e5 Part of collection Student theses Document type bachelor thesis Rights © 2022 Dean Nguyen Files PDF Research_Paper_FINAL.pdf 2.25 MB Close viewer /islandora/object/uuid:f6a51608-5186-4a33-acba-dbe73685a4e5/datastream/OBJ/view