title: The gradient boosting method modification to forecast demand for individual types of goods
abstract: The subject area of retail sales is characterized by the presence of many different noises and anomalies in the data. The use of machine learning methods in forecasting such data leads to a decrease in the accuracy of forecasts and the occurrence of the overfitting effect. Despite the adjustment of the existing parameters in the existing gradient boosting algorithms, this problem remains and there is a need for additional improvements to build more accurate forecasts. Purpose of the study: impro
journal: H&ES Research