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Model validation is defined to mean "substantiation that a computerized model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model". [3] A model should be built for a specific purpose or set of objectives and its validity determined for that purpose. [3]
ASP.NET is a server-side web-application framework ... ASP.NET MVC – allows building web pages using the model–view ... Automatic input validation;
Independent Software Verification and Validation (ISVV) is targeted at safety-critical software systems and aims to increase the quality of software products, thereby reducing risks and costs throughout the operational life of the software. The goal of ISVV is to provide assurance that software performs to the specified level of confidence and ...
Residual plots plot the difference between the actual data and the model's predictions: correlations in the residual plots may indicate a flaw in the model. Cross validation is a method of model validation that iteratively refits the model, each time leaving out just a small sample and comparing whether the samples left out are predicted by the ...
Model checking is also studied in the field of computational complexity theory. Specifically, a first-order logical formula is fixed without free variables and the following decision problem is considered: Given a finite interpretation, for instance, one described as a relational database, decide whether the interpretation is a model of the ...
ASP.NET MVC is a web application framework developed by Microsoft that implements the model–view–controller (MVC) pattern. It is no longer in active development [ citation needed ] . It is open-source software , apart from the ASP.NET Web Forms component, which is proprietary .
Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]