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Cross validation process is a step undertaken prior to geostatistical estimation to ensure the validity of the variogram parameters.
The Cross Validation process was carried out to confirm the predicting power of the QSAR model.
After a ten-fold cross validation process, the classification algorithm yielded results on accuracy which are depicted in Table 3 (stage 1).
The model with the lowest prediction error generated through the cross validation process was chosen to represent the best outcome for each BCRP polymorph. .
The k-fold cross validation process is then repeated k times, with each k subgroups used as a training set exactly once.
The lake water quality prediction maps showing the concentration distribution generated from the surface map developed from the cross validation process discussed above.
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The cross-validation process is performed to verify the accuracy of the ensemble structures.
For this study model, these problems were addressed with the k-fold cross-validation process.
Our grid search consisted of 21 × 6 = 126 points, and we repeated the cross-validation process 50 times.
Such cross-validation process was performed for three times, so as to ensure each partition was validated.
Finally, the results obtained by each algorithm are averaged over 30 runs using the stratified 10-fold cross-validation process.
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