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Logistic models achieved an overall prediction success rate of around 90percentnt with contrast weighted visual magnitude being a key variable.
A consensus model based on this decision tree and two SVM models achieved an accuracy of 79.6 ± 0.2 % for the whole set.
We found that only one of the 50 resulting models achieved an R2 greater than the L1 13-gene model (i.e., P = 0.02) (Table 8).
Recently published machine learning models achieved an area under receiver operating characteristic curve (AUC) around 0.85 0.95 for Class I HLAs and 0.75 0.85 for Class II HLAs.
It can be seen that all models achieved an acceptable discrimination (an AUC greater than 0.70) both in the development and the validation set.
As shown in Table 4, ab initio models achieved an average CA-RMSD 5.48 Å after MD simulations of 100 ps.
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The SVM models achieved a Spearman correlation of 0.54 between measured RC and pRC.
None of the models achieved a successful description of the additivity.
All logistic regression models achieved a rho-square between 0.20 and 0.40 suggesting they were very good (fitting) models for predicting job satisfaction.
By comparison, both GRS-DQ2.5 and GRS-DQ2.5-imputed models achieved a ratio of approximately 2 1 at the same sensitivity as DQ2.5-zygosity.
Moreover, the prediction models achieve an acceptable performance when estimating only one exogenous variable with a more significant impact on forecast performance for the accurate container gross weight.
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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