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The "All available features" model achieved the highest decoding accuracy out of the six models tested.
316 (2004) 1050 1058) our RFR model achieved the best performance of all.
Using MTS, our model achieved the following simulation rates: 12.5, 25.0, 35.5 μs/day for Exp-S and 9.09, 6.25, 14.29 μs/day for Exp-M on Tianhe-2, CS-Storm 16-K80 and Stampede K20.
Thus, the developed consensus model achieved the experimental accuracy of the MP data (Fig. 6).
Moreover, the combined TF + Motif model achieved the highest predictive accuracy (AUC = 0.818).
Of the three regression models, the spatial error model achieved the best fit (R2 = 0.28).
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The results show that the DNN model achieves the best balanced precision and recall.
The empirical results show that the proposed model achieves the improved forecasting accuracy.
Based on my research, I believe the model achieves the goal.
The same feature set is also feasible for the regression problem, and the linear regression model achieves the best performance on median absolute error of 0.3 rating points.
The results show that our RF model achieves the best performance.
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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