Sentence examples for a more robust prediction from inspiring English sources

Linear regression between urban and population growth offered a more robust prediction of urban growth in Morelia.

Ensemble models were also developed that combined the predictions of the two modelling approaches to obtain a more robust prediction.

Technically, the implementation of high-throughput sequencing analysis and genomic technologies are rapidly becoming part of standard practice, which may ultimately allow a more robust prediction of response to immune checkpoint blockade.

We present a novel approach to apply machine learning techniques to build a more robust prediction model for band-gap energies (BG-E) of chalcopyrites, a class of materials for energy applications in the fields of solar energy, photocatalysis, and thermoelectrics.

This result indicates that each latent graph provides prediction knowledge from a specific aspect, and their combination leads to a more robust prediction.

However, there have been few genomic nuggets that enable a more robust prediction of diabetes than is achieved by using common environmental risk factors and none that clarify the peculiar ethnic proclivities of type 2 diabetes.

This modification of the data set enabled the development of a much more robust prediction model (model 2) which predicted correctly the initial parts of the progress curves.

Precision estimates of 21.7 and 20.0% were obtained for these two models, indicating that the use of creatinine in addition to weight results in a marginally more robust prediction of Cr-EDTA clearance.

They also suggested that CTC assessment might have advantages over radiographic evaluation, including higher reproducibility due to lower interreader variability, useful results at an earlier time, and more robust prediction of survival [ 5].

More robust prediction of clinical diagnosis may require a combination of a number of risk and protective factors, including response to gaze.

Compared with the single model, the new ensemble model can provide more robust prediction results and can be considered to be an alternative choice for quantitative analytical applications.