Skip to main content

Science – Society – Technology

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