IEEE Systems, Man and Cybernetics Magazine - April 2023 - 44

Dataset-10. The test set has a slightly higher range of X1 in
training and validation sets.
Common Characteristics of Datasets
Dataset-1 to Dataset-8 have 5,000 samples for training,
2,000 samples for validation, and 2,000 samples for the test
sets. Dataset-9 and Dataset-10 have 200 samples each for
train, test, and validation subsets.
Besides all patterns mentioned in the dataset sections,
the range of inputs and outputs in some datasets is much
higher than unity. Algorithm developers need to ensure
that their algorithm works properly for different ranges of
inputs and outputs.
Performance Parameters
The purpose of developing this dataset is to help regression
and UQ algorithm researchers. Researchers consider
several performance criteria to judge the quality of the
model. Popular performance criteria of regression and UQ
are as follows.
Regression
Common performance parameters in regression are the
mean square error (MSE), the root-mean-square error
(RMSE), mean absolute error, mean-absolute-percentage
error, sum-square-error, and so on. MSE and RMSE are

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