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Following a standard n-fold cross-validation approach, each fold was used once as the outer testing set where the remaining folds were used as the outer training set.
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Then the 'leave-one-out' cross-validation (LOO-CV) and an outer test set including 18 outer samples were used to validate the QSAR model.
Equation 6 shows how we calculated the prediction error for a single test set whether an inner or an outer test set.
Each data partition sets aside 10% of the data set (outer test set) to measure the performance of the predictive model generated from the other 90% of the data (outer training set).
This was because, also in forecast analysis, the purpose was to predict consecutive time points, in opposition to cross validation where the outer test set points were not consecutive.
A pseudocode is provided in Table 2. To prevent overfitting, we effectively partitioned the data into three: a feature extraction training subset (inner training set); a model size selection and variable stability evaluation subset (inner testing set); and an outer test set for performance estimation of models trained on the outer training set, which comprised the inner training and testing sets.
After the test set elements (outer test sets) which we used also in the Kinetic modeling Section were removed from the dataset, the remaining part was again subjected to a division of test (inner test sets) and training sets for a 10-fold cross validation with 10 repetitions.
The cyclic optimization phase: Outer loop: testing of codebook vectors f j i), i = 1,..., K, spanning the K-dimensional subspace.
The cyclic optimization phase: (a) Outer loop: testing of codebook vectors f j i), i = 1,..., K, spanning the K-dimensional subspace.
Figure 6a showed the pictures of the outer bending test using a lab-made bending test system.
Notably, the optimized IAIA multilayer had a constant resistance change (ΔR/R0) under repeated outer bending tests with a radius of 10 mm.
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