Sentence examples for model importance from inspiring English sources

A discrimination criterion is developed based on posterior model probability that directly uses data to evaluate model importance.

Both F-values and model importance scores show that Site Index derived from LiDAR to be the most useful predictor of the 300 Index of the LiDAR metrics (Fig. 3).

The simulation results prove the model importance with a figure of merit greater than 50%, what means the amount of correctly predicted change is larger than the sum of any type of error.

All together, temporal variables are the most important variables in the model: they amount to around ({sim} 51%) of the importance in our predictive model (see Figure 3), while intensity variables giving ({sim} 36%) of the importance and finally structural and intimacy variables representing less than ({sim} 10%) (each) of the model importance.

The accuracy of this classification is then used as a metric of model importance, including the predictive value of the model in unseen data through the use of N-fold cross validation [ 3].

For each model, importance scores for all regulatory relationships were derived and we focused on the 200 000 highest scored regulatory relationships (in the DREAM5 challenge, only the first 100 000 predicted regulations were considered for the competition, we relax the cutoff so that true predictions are less likely to be excluded due to this parameter setting).

The uncertainty of numerical models, importance of high fidelity experimental data, and the challenges associated with simulating and validating large production-type problems are presented.

We calculated approximate marginal likelihoods for each coalescent model via importance sampling using the harmonic mean of the sampled likelihoods (with the posterior as the importance distribution) [40].

We calculated approximate marginal likelihoods for each model via importance sampling using the harmonic mean of the sampled likelihoods (with the posterior as the importance distribution; see [ 27]).

Fig. 6 Random forest model variable importance.

From the finite element model, the importance of initial imperfections and stiffness of connections is identified.