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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