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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]
The validation is performed against the written specifications or predetermined expectations, based upon their historical data/evidences that are documented/recorded. If any critical data is missing, then the work can not be processed or can only be completed partially. [17] [27] [28] The tasks are considered necessary if:
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]
Industrial process data validation and reconciliation, or more briefly, process data reconciliation (PDR), is a technology that uses process information and mathematical methods in order to automatically ensure data validation and reconciliation by correcting measurements in industrial processes.
An example of a data-integrity mechanism is the parent-and-child relationship of related records. If a parent record owns one or more related child records all of the referential integrity processes are handled by the database itself, which automatically ensures the accuracy and integrity of the data so that no child record can exist without a parent (also called being orphaned) and that no ...
Cross-validation, [2] [3] [4] sometimes called rotation estimation [5] [6] [7] or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set. Cross-validation includes resampling and sample splitting methods that use different ...
Validation may refer to: . Data validation, in computer science, ensuring that data inserted into an application satisfies defined formats and other input criteria; Emotional validation, in interpersonal communication is the recognition, the affirmation, the acceptance of the existence of expressed emotions, and the communication, the acknowledgement, of this recognition with the emoter(s ...
In statistics, model validation is the task of evaluating whether a chosen statistical model is appropriate or not. Oftentimes in statistical inference, inferences from models that appear to fit their data may be flukes, resulting in a misunderstanding by researchers of the actual relevance of their model.