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The coefficient of variation was 3.02% (<10%), indicating that the model had high reliability.
The correlation coefficient was 0.995 which mean the model had high fitting capacity.
Secondly, the external validation, it exposed that the students learning with model had high levels of cognitive skills and critical thinking and achievements.
The actual measurements on-site and the final calculation results showed that the established model had high calculation accuracy and was beneficial for interstand tension control of tandem cold rolling process.
Statistical analysis revealed that the proposed generalized SVR-based model had high prediction accuracy with an average absolute relative error (AARE) of 3.82%, root mean square error (RMSE) of 0.0717, leave-one-out cross validation (Q2LOO) of 0.9975 and mean relative error (MRE) of 0.0288 on the training data.
The model had high sensitivity (81%) and specificity (80%).
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A closer look at Fig. 10 indicates the global model had higher modeling efficacy than the seasonal model.
From the kinetic data (Table 2), it was observed that the pseudo second-order model had higher R 2 values than the pseudo first-order model.
The predictions from our function-specific classifier model had higher overlap with RNAi results than the canonical model in all situations examined.
Heart rate showed a similar pattern, although at baseline animals in the sepsis model had higher heart rates (P = 0.001).
Accuracies of GEBV in Table 2 and Figures 2 and 3 show that the threshold model had higher accuracies than linear model analyses when analyzing categorical data.
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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