Description Usage Arguments Value Note Author(s)
Implementation of the Generalized Regression Neural Network architecture for Single and Multi-Output Regression with Uncertainty Estimation Using Conformal Prediction.
1 2 | grnnet(x, y, alpha = 0.05, crossvalidation = T, kfold = 5,
seed = 1984)
|
x |
a matrix of numeric inputs |
y |
a matrix of numeric outputs |
alpha |
a numeric value. confidence level (i.e, 0.05) |
crossvalidation |
a logical indicating if k-fold cross-validation should be performed. Default is TRUE (T) |
kfold |
number of fold for k-fold cross-validation |
seed |
seed for the random number generator. Controls reproducibility of k-fold cross-validation |
a trained generalized regression neural network
The network training is performed with an internal leave-one-out sampling loop and derivate free optimization (Brent's algorithm). The bounds of the sigma parameter, the spread of the squared exponential kernel, are initialized following Tomandl & Schober (2001) based on the proprieties of the distance matrix between cases. The internal loop also computes the conformity factors needed to compute the prediction intervals based on conformal prediction.
David Navega
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