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10.1016/j.chaos.2020.110055 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.110055 C7324351!7324351!32834608     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chaos+Solitons+Fractals 2020 ; 139 (ä): 110055 Nephropedia Template TP {{tp|p=32834608|t=2020. An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data |pdf=|usr=}}{{32834608}} gab.com Text An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834608 #FOAvip 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 best in-sample performance, but also yielded the worst out-of-sample predictions, a typical example of overfitting in a machine learning model. On the other hand, the linear Kernel function performed badly in-sample but generated the best out-of-sample forecasts. The findings of this paper provide an empirical assessment of fundamental concepts in data analysis and evidence the need for caution when applying machine learning models to support real-world decision making, notably with respect to the challenges arising from the COVID-19 pandemics. Twit Text FOAVip An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834608 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32834608 #FOAvip English Wikipedia <ref>{{Cite pmid|32834608}}</ref> An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data #MMPMID32834608 Peng Y; Nagata MH Chaos Solitons Fractals 2020[Oct]; 139 (ä): 110055 PMID32834608show ga 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 best in-sample performance, but also yielded the worst out-of-sample predictions, a typical example of overfitting in a machine learning model. On the other hand, the linear Kernel function performed badly in-sample but generated the best out-of-sample forecasts. The findings of this paper provide an empirical assessment of fundamental concepts in data analysis and evidence the need for caution when applying machine learning models to support real-world decision making, notably with respect to the challenges arising from the COVID-19 pandemics. äAn empirical overview of nonlinearity and overfitting in machine learn(epmc).pdf DeepDyve Pubget Overpricing