To the question of optimal division of the full sample during machine learning - Suvalov, Valentin O. (Journal of the Belarusian State University. Economics, 2021)
The article is devoted to modern approaches to dividing of a data set into training, control and test samples, used in the process of machine learning for forecasting purposes. The actual issue of choosing the optimal division of the entire available set of data into named above samples is considered. The author analyses the results of the operation of a software algorithm developed to find the optimal division of a data set into isolated samples for the purposes of machine learning of predictive models. A recommendation to separate 80 % of the total sample to minimise the forecast error of the developed models is given.
Keywords: machine learning; big data; data analysis; econometric tools; training sample; control sample; test sample