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The experimental results on synthetic and real-world benchmark datasets show that the proposed models produce good performance compared with current regression algorithms and provide superior robustness.
Our models produce good predictive performance along with useful explanatory information.
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The model produced good agreement with the measured values.
The latter model produced good fits to the experimental results.
Regression coefficients (R2), root mean square (RMSE) values and residual plot strongly suggested that Weibull model produced good fit to the data.
Empirical results from simulations demonstrated that the proposed SBLLM model produced good generalization performance, with high stability and consistency, which are requisites of good prediction models in reservoir characterization and modeling.
The model produced good results with (R2 = 0.9117 and a RMSE = 1.8297°) in estimating the location of the (Po) point in various natural and man-made beaches with different degrees of wave directionality.
Results of simulations reveal that although the continuum porous media model produces good agreement with experimental velocity profiles after the monolith, it does not describe accurately the effects of turbulence inside the monolith.
The 2TM model produced good match to the measured heat flux at both interior and exterior surfaces for both walls, unlike a one thermal mass model (1TM); Bayesian model comparison strongly supported the 2TM over the 1TM model to accurately describe the observed data.
This model produced good fit across demographically diverse cohorts, as well as over time.
Based on leave-one-out cross-validation test, the model produced good prediction accuracy (94.1% sensitivity, 92.6% specificity, and 92.8% positive predictive value).
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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