An Application of a Three-Stage XGBoost-Based Model to Sales Forecasting of a Cross-Border E-Commerce Enterprise

Table 1

The description of parameters in the XGBoost model.

Type of parameters	Parameters	Description of parameters	Main purpose
Booster parameters	Max depth	Maximum depth of a tree	Increasing this value will make the model more complex and more likely to be overfit
	Min_child_weight	Minimum sum of weights in a child	The larger the min_child_weight is, the more conservative the algorithm will be
	Max delta step	Maximum delta step	It can help make the update step more conservative
	Gamma	Minimum loss reduction	The larger the gamma is, the more conservative the algorithm will be
	Subsample	Subsample ratio of the training instances	It is used in the update to prevent overfitting
	Col sample by a tree	Subsample ratio of columns for each tree	It is used in the update to prevent overfitting
	Eta	Learning rate	Step size shrinkage used in the update can prevent overfitting
Regularization parameters	Alpha	Regularization term on weights	Increasing this value will make the model more conservative
	Lambda
Learning task parameters	Reg: linear	Learning objective	It is used to specify the learning task and the learning objective
Command line parameters	Number of estimators	Number of estimators	It is used to specify the number of iterative calculations