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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]
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.
Inspection is a verification method that is used to compare how correctly the conceptual model matches the executable model. Teams of experts, developers, and testers will thoroughly scan the content (algorithms, programming code, documents, equations) in the original conceptual model and compare with the appropriate counterpart to verify how closely the executable model matches. [1]
Such final external validation requires the use of an acceptance test which is a dynamic test. However, it is also possible to perform internal static tests to find out if the software meets the requirements specification but that falls into the scope of static verification because the software is not running.
Test in the small: a test that checks a single function or class ; Test in the large: a test that checks a group of classes, such as Module test (a single module) Integration test (more than one module) System test (the entire system) Acceptance test: a formal test defined to check acceptance criteria for a software Functional test
Highly abstract or novel new concepts can be difficult to understand without concrete examples. [citation needed] Specification by example is intended to construct an accurate understanding, and significantly reduces feedback loops in software development, leading to less rework, higher product quality, faster turnaround time for software changes and better alignment of activities of various ...
The validation process begins with validation planning, system requirements definition, testing and verification activities, and validation reporting. The system lifecycle then enters the operational phase and continues until system retirement and retention of system data based on regulatory rules.
Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]