The phrase "assessing model" is correct and usable in written English.
It can be used in contexts where you are evaluating or analyzing a particular model, such as in research, data analysis, or machine learning.
Example: "The team is currently assessing the model to determine its accuracy and effectiveness in predicting outcomes."
Alternatives: "evaluating model" or "analyzing model".
SkyBase uses quantitative reliability criteria for assessing model quality ("modelability") to decide whether a sequence relationship is likely to be meaningful.
One technique for assessing model quality is fivefold cross validation, which gauges how well the model is expected to generalize to an independent data set.
Procedures for assessing model predictive performance or accuracy are discussed, including model discrimination, calibration, and power.
We then emphasize assessing model prediction using inundation maps derived from remotely sensed data.
This includes careful assessment of different metrics for performance of visual saliency models, and identification of remaining difficulties in assessing model performance.
TRACE thus becomes a tool for planning, documenting, and assessing model evaludation, which includes understanding the rationale behind a model and its envisaged use.
Meanwhile, we have numerically evaluated the LB models for a standing-shear-wave problem, which is designed specifically for assessing model accuracy by excluding the influence of gas molecule/surface interactions at wall boundaries.
The work reported in this paper extends previous research by comparing the performance of various data fusion neural network architectures and assessing model performance for various probe vehicle penetration rates and loop detector configurations.
In these case studies we illustrate methods to make the choices, typically by i) comparing the results of different model versions, ii) assessing model validity or iii) indicating the sensitivity of models for different simplifications.
We believe there is still plenty of scope for extensions and novel applications of the current set of methods, but that further investigation of study design, model limitations, and assessing model, fit should be research priorities over the next decade.
This can assist with identifying priorities for refining the model and assessing model robustness.
