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Our proposed model also outperforms XYJ model.

At pressure and temperature up to 95.04 MPa and 429.3 K, respectively, the model also outperforms two-parameter EoSs with average absolute relative errors of 1.65% and coefficient of determination of 0.992.

Besides, the proposed model also outperforms other machine learning methods.

This model also outperforms the 10-clusters model determined by the likelihood-based clustering approach.

This model also outperformed the polynomial model remarkably in terms of predicting error, residuals range and the correlation coefficient between the experimental and predicted MRR values.

The normative model also outperformed the model with perfect integration to a stabilizing boundary that freely varied by objective hazard rate (BIC was lower for 9 of 13 subjects, −4.305 ± 2.662, p < 0.05).

The 36-parameter regional regression models also outperformed a 16-parameter national SPARROW model of suspended-sediment discharge and indicate that mean annual sediment loads in the eastern United States generally correlates with a combination of basin area, land use patterns, seasonal precipitation, soil composition, hydrologic modification, and to a lesser extent, topography.

Interestingly, weather models also outperformed climate models for sedentary species.

While the benefits of incorporating short-term weather in SDM may be intuitive for highly dispersive species, we have shown that the weather models also outperformed the climate models for sedentary bird species, suggesting that our weather modelling technique could be beneficial for species more affected by short-term weather fluctuations and variability than long-term averages.

For disease mortality, the gene expression models also outperform the model based on clinical parameters (model A, median C-index of 0.59), though this is not the case for disease recurrence (median C-index of 0.58 for clinical model).

Our model could also outperform different techniques based on co-expressed gene modules.