Sentence examples for enhances model performance from inspiring English sources

In order to assess whether merging the chemical and the target space in a single PCM model enhances model performance, we trained two Random Forest (RF) models using either: (i) only compound descriptors (Family Quantitative Structure-Activity Relationship -QSAR-) [53], or (ii) only target descriptors (Family Quantitative Sequence-Activity Modeling -QSAM-) [53].

However, detailed heat exchanger models can now be employed in dynamic simulations to enhance model performance significantly while maintaining reasonable computational times.

As the most predictive PCM model exhibited moderately high (R^{2}_{0 test}) and (q^{2}_{textit {test}}) values, as well as moderately low RMSEtest values (Table 2A and Figure 4), we explored the possibility of enhancing model performance by combining different models into a more predictive model ensemble (Table 2D, E and Figure 4).

Last, we provide recommendations for future research to enhance model performance and improve our ability to forecast and prepare for the effects of climate change on DF.

Therefore, if the addition of these pathology clinical variables to a predictive model with variables attained solely from administrative data does not enhance model performance, their inclusion should be avoided.

Flood error correction (FEC) and multi-model composition (MC) methods are two effective ways to enhance the model performance.

In addition, it was proved that the implementation of MPCA with dynamic neural network could enhance the model performance.

Simulation results and their intercomparison indicate that including additional topographic variables in the input vector can enhance the model performance.

The proposed three-step analytical approach enhanced predictive model performance for CHF readmissions.

This can also occur at the other extreme when the prediction/classification problem at hand is more complex and requires a much larger training size than is currently available, to significantly enhance the model performance.

In fact, in models only including session and individual GIS variables (i.e., Table 4), the wind-weighted covariates demonstrated marginally weaker associations with NO2 concentrations for Class 2 and Class 3 roadways, with a modest improvement for Class 1 roadways that did not enhance multivariate model performance.