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Exact(10)
5-fold cross validation shows that the SVM models are stable across the different descriptor sets whereas the other modelling algorithms are susceptible to a change in descriptors.
If the models are stable, then their re-estimated coefficient should be also similar.
It can be seen clearly that the generated models are stable and predictable statically.
In addition, the between-trial differences in mean EMG-force relationships suggest that the models are stable over time.
Although the SVM models are stable for 1,200 dpi resolutions, the model stability performs poorly when resolution is low (300 to 500 dpi).
The cross-validation showed that the models are stable and the values of R2 and RMSE are similar for the test set, the training set and the complete set.
Similar(50)
The standard deviations of the AUC ranged from 0.000 to 0.019 suggesting that the models were stable, i.e. the selection of non-DDI drugs did not affect the results.
The LOOCV analysis indicated that the RF5 and RF10 models were stable resulting RMSE of 13.61 and 13.38% and Bias of −0.09 and −0.27% for the models derived from the 5 and 10 pulses m−2 datasets, respectively.
This clearly indicated the RMSD values of the protein backbone atoms and ligand atoms were always kept around 1.5 Å and 0.4 Å respectively, which showed that the MD-simulated binding models were stable.
Indeed, in contrast to fibroblasts or lymphocytes cell lines from FRDA patients, which show a large variability and a lack of reproducibility depending on the culture conditons, the biochemical phenotype of the "humanized" cell models is stable over multiple passages.
Furthermore, the models were stable to the inclusion of additional cohorts.
More suggestions(15)
models are strong
templates are stable
samples are stable
models are sustainable
models are well established
models demonstrate stable
models infer stable
models produce stable
models are ineffective
models are tiny
models are available
models are elderly
models are scarce
models are different
models are scalable
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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