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Table 12 Detailed nomenclature for machine learning-related variables
From: A machine learning approach for mapping the very shallow theoretical geothermal potential
Symbol | Unit | Description |
|---|---|---|
\(\varphi\) | (–) | 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 |