Skip to main content

Table 4 List of the optimal parameters and description of the XGBoost model

From: Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model

Parameters Value
Booster ‘gbtree’
Objective ‘reg: squared error’
Early_stopping_rounds 5
Eval_metric ‘rmse’
Min_child_weight 2
Subsample 0.4
Colsample_bytree 0.6
Eta 0.05
Nrounds 200
Depth 2