An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data

Volume: 139, Pages: 110055 - 110055
Published: Oct 1, 2020
Abstract
In this paper, we applied support vector regression to predict the number of COVID-19 cases for the 12 most-affected countries, testing for different structures of nonlinearity using Kernel functions and analyzing the sensitivity of the models’ predictive performance to different hyperparameters settings using 3-D interpolated surfaces. In our experiment, the model that incorporates the highest degree of nonlinearity (Gaussian Kernel) had the...
Paper Details
Title
An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data
Published Date
Oct 1, 2020
Volume
139
Pages
110055 - 110055
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