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The results also indicate that regular SVR models perform slightly better than regular ANN models.
The full models perform slightly worse than the stepwise or single models, an example of potential over-fitting (i.e. not generalizing well to a new data set).
Also, the results show that in general n-mer models perform slightly better than the corresponding FBP models regardless of the training (CpG+ or CpG− datasets) (Figure 3).
Our study has now evaluated performance of most developed prediction models for future diabetes in an external population and shows that most basic models perform well to identify those at high risk of diabetes and that extended models perform slightly better.
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Cao ([2006]) found that while the two constrained models performed slightly better than the unconstrained tree model in predicting tree and stand attributes, the disaggregation method provided the best predictions of tree- and stand-level survival and growth.
It was observed that the lumped input data models performed slightly better because; apart from reducing the noise in the data, the better techniques and their training approach, appropriate selection of network architecture, required inputs, and also training testing ratios of the data set.
The SIC (0.5%), prior-year expenditures (0.6%), the CDPS (0.9%), and the RxRisk-V (1.3%) models performed slightly better.
Again the models performed slightly poorer than the full, data-driven model in the ECRIC dataset, but somewhat better in the WMCIU validation dataset.
Using literature-reported parameter values, the mechanistic LBL and LBLX models performed slightly better overall than the empirical SF model, and the individual model-predicted AER from these three models agreed reasonably well to the 642 daily AER measurements across 31 detached homes from the RTP Panel Study.
Moreover, NRTL model performed slightly better than UNIQUAC model since the RMSD was smaller.
Their comparison result showed that the hybrid model performed slightly better than using only one model.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com