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The generation of 10 different splits should avoid a validation bias induced by the random splitting procedure.
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Several terms have defined: A: set of annotated GO functions (in test set) P: set of predicted GO functions F: GO functions in train set So, we can establish: (1) (2) (3) (4) then, define: (5) (6) The variance of the reconstructed annotation was computed in order to see if it is affected by the random split choice.
Fig. 2 Performance of the different methods in the random split validation, grouped by underlying algorithm and colored by metric used.
How to do the random split is also an important issue that has been addressed by many in classification/prediction literature.
All methods performed worse than on the random split benchmark.
This was done for both the random split set and temporal split set.
For development of the DNT15, Spearman's correlations were used in the random split sample analysis.
The random splits of the Norway/Stanford, Korean, and Zhao/Langerød datasets were done once.
This is to be expected since the random split approach incorporates information regarding the generalizability of the model (via the validation stage) in addition to model stability provided by the bootstrapping.
The support vector machine produced 82% classification accuracy for organic carbon levels by repeated random splitting of the whole dataset.
Przewlocki et al. [ 10] performed validation of their model by random splitting of the data.
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