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Training data was further split, with 20% set aside for validation of each epoch to optimize the network through back propagation with gradient decent.
Five percent of the reflections were set aside for validation.
In addition, some subjects could be set aside for validation of the model in order to assess whether overfitting is a concern.
This data set was randomly divided with a 90% split between the training (70%) and test sets (30%), and the additional 10% set aside for validation.
Such external validation, while providing a sense of real-world application, must incorporate assurance that samples set aside for validation are representative.
For i = 1, K, the i-th subset was set aside for validation, while all the remaining K-1 subsets were used to obtain estimates Σ G ψ and Σ E ψ for all values of ψ considered.
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Split analysis (a randomly selected 70% of study subjects were used for model development; the rest were set aside for model validation) was performed and measures of model performance were obtained from the validation set to avoid overfitting.
Approximately two-thirds of the data was used for model fitting (n = 165 plots for Kaingaroa; n = 312 plots for Tairua) while the remaining one-third was set aside for model validation (n = 82 plots for Kaingaroa; n = 157 plots for Tairua).
Model training occurred with 126 records; the other portion was set aside for model validation.
The non-cartilage arrays were a subset of the training dataset set aside for this validation only.
In the initial stages, all refinement was conducted to a resolution of 1.4 Å using Refmac5, with 5% of the total reflections randomly set aside for cross validation.
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