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Geothermal Energy Cover Image

Table 12 Detailed nomenclature for machine learning-related variables

From: A machine learning approach for mapping the very shallow theoretical geothermal potential

\(\varphi\)(–)Generic predictive function learned from training process
B(–)Number of trees in a Random Forest
d(–)Number of features (predictors) in the training data for an ML task
\(E_{{\text{R}}}\)(–)Root Mean Square Error
\(E_{{\text{NR}}}\)(–)Normalized Root Mean Square Error
K(–)Number of folds consider in fold cross-validation
m(–)Number of variables to consider to split a node v in a decision tree
N(–)Number of training points (size of training data)
\(N_{{\text{test}}}\)(–)Number of testing point (size of testing data)
\({\mathbb {R}}\)(–)Set of real numbers
\({\mathbf {x}}\)(var. unit)Generic input vector (realization of \(X_{1},...,X_{d}\))
\(X_{1},...,X_{d}\)(var. unit)Generic input variables
y(var. unit)Generic output value (label, realization of Y)
Y(var. unit)Generic output variable
  1. (–) signifies no unit. (var. unit) signifies that the unit is defined by the quantity of interest represented by the variable