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Table 2 Information about hyper-parameters related to Ridge-regression, Random Forest and XGBoost models

From: Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

Model Hyper-parameter Range Optimum
Ridge-Reg Alpha [0.001, 100] 0.01
Random Forest Max_depth {5,8,10,12,15} 12
Random Forest N_estimators {100,500,1000} 500
Random Forest Min_samples_leaf {1,2} 2
Random Forest Min_samples_split {2,3} 2
XGBoost Max_depth {5,8,10,12} 8
XGBoost N_estimators {100,500,1000} 500
XGBoost Learning_rate {0.01,0.05,0.1,0.2} 0.05
XGBoost Gamma {0.1,1,10} 10
XGBoost Reg_lambda {0.1,1,10} 10