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Exact(60)
Bootstrap validation was performed to evaluate the performance of the model.
A sub-group analysis was performed to examine the performance of the model in the sub-groups in Table 5.
To effectively test the performance of the model, we performed the "leave-one-out" procedure described above and obtained 71% sensitivity and 100% specificity.
The records at the other two locations were used for calculating the performance of the model.
The performance of the model using the NER tags predicted by our NER classifier is here.
The performance of the model is evaluated using the proportion of the deleted edges that it correctly placed.
Based on the performance of the model since 1987, this year's forecast error was one of the largest.
Overall, the performance of the model is considered good.
To effectively test the performance of the model, each subject was iteratively excluded from the training set and predicted on the basis of the other subjects.
The performance of the model was compared with BCFBAF v3.00.
It adversely affect the performance of the model.
More suggestions(16)
the performance of the sample
the functioning of the model
the performance of the template
the performance of the system
the performance of the pattern
the outputs of the model
the performance of the specimen
the results of the model
the performance of the models
the performance of the specimens
the performance of the year
the heydey of the model
the symmetry of the model
the derivation of the model
the selection of the model
the robustness of the 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