How does sample weighting improve learning curve fitting?
G.F.M. den Hollander (TU Delft - Electrical Engineering, Mathematics and Computer Science)
T.J. Viering (TU Delft - Electrical Engineering, Mathematics and Computer Science)
C. Yan (TU Delft - Electrical Engineering, Mathematics and Computer Science)
O.T. Turan (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
Learning curves plot the performance of a machine learning model against the size of the dataset used for training. Curve fitting is a process that attempts to optimize algorithm parameters by minimizing the error in its loss function, thereby achieving the best possible fit to the data. We apply various sample weighting techniques to the curve fitting process and evaluate whether the resulting weighted curves can significantly improve the performance of the model. We explore whether adjusting the magnitudes of these weights can further improve the fit of the curve. The results demonstrate that each sample weighting method, as well as larger weight magnitudes, can significantly improve error rate prediction for anchors beyond the range of the observed data.