Sentence examples for model development data from inspiring English sources

Many mortality prediction models exist but have shown deficiencies in model development (data comprehensiveness, validation) and in practicality.

The model development data were divided into two sets.

Topics covered will include: when and why to conduct evaluation; types of evaluations; key aspects of an evaluation, including logic model development, data sources and data collection, analysis and reporting, and guiding resultant programmatic change; and tips for feasibly and effectively implementing evaluation at a program or organization.

Model development, data collection, and use of strategic results of relevance to key stakeholders and community decision makers require their direct engagement in the modeling exercise that can be easily accommodated in an open process that promotes model verification and viability.

Of these, 2,937 sample plots containing 23,813 measurements aged between 3 and 61 years old, were used as a model development data set, and a further 702 plots containing 5,699 measurements were used as a validation data set.

First, a different, proprietary algorithm was implemented, and second, all stage I samples analyzed across both rounds one and two were used to increase the size of the model development data set (Figure 1).

Among the activities of translational biomedical research and clinical practice, computational approaches, including data curation and management, algorithm and model development, multidimensional data integration, data visualization, and high performance computing, provide fundamental support.

We combined the data from the two studies used in the initial model development with data from three additional published population-based studies.

"Model development and data collection should be more integrated.

Currently, these are the time which has to be spent on the model development including data pre-processing and model building together with the effort which needs to be spent on periodical performance assessment and re-training of the model.

The customization reduces the algorithmic knowledge requirements on users and allows users to concentrate on model development and data analysis, rather than programming and compatibility issues.