Print Email Facebook Twitter The influence of the dimensionality on the parameters of the learning curve model Title The influence of the dimensionality on the parameters of the learning curve model Author Mereuta, Andrei (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Pattern Recognition and Bioinformatics) Contributor Viering, T.J. (mentor) Krijthe, J.H. (graduation committee) Yue, Z. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-28 Abstract Learning curves in machine learning are graphical representations that depict the relationship between a model's performance and the amount of training data it has been exposed to. They play a fundamental role in obtaining the knowledge and skills across a range of domains. Although there are already quite some researches studying machine learning curves, explaining the importance and practical application of learning curves, we still know very little about the factors that influence the parameters of the learning curve. The aim of this research is to give a better understanding of different factors affecting the parameters of the learning curve. Specifically, we are interested in how the dimensionality of a dataset can influence the parameters of the learning curve. Since learning curves are useful and have several applications, such as estimation of the time required to complete production runs, we would like to know if the dimensionality has any effect on the shapes of learning curves. To conduct the research I applied principal component analysis (PCA) three times with different amount of information preserved to reduce number of dimensions on several datasets and analysed the changes in the parameters of the obtained learning curves. The research showed that potentially there might be some relation between dimensionality and shape of the curve, but only in cases of specific machine learning model. The amount of experiments conducted is not sufficient to make solid conclusions and it is advised to continue with proposed experimental setup, but train machine learning models on increased number of datasets. Subject Dimensionality effectLearning curveinfluence parameters` To reference this document use: http://resolver.tudelft.nl/uuid:6a728c60-1dac-40cc-951c-1a23f9fd995a Part of collection Student theses Document type bachelor thesis Rights © 2023 Andrei Mereuta Files PDF CSE3000_Final_Paper_Templ ... ereuta.pdf 149.34 KB Close viewer /islandora/object/uuid:6a728c60-1dac-40cc-951c-1a23f9fd995a/datastream/OBJ/view