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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]
Checks that the data is in a specified format (template), e.g., dates have to be in the format YYYY-MM-DD. Regular expressions may be used for this kind of validation. Presence check Checks that data is present, e.g., customers may be required to have an email address. Range check
PyCharm – Cross-platform Python IDE with code inspections available for analyzing code on-the-fly in the editor and bulk analysis of the whole project. PyDev – Eclipse-based Python IDE with code analysis available on-the-fly in the editor or at save time. Pylint – Static code analyzer. Quite stringent; includes many stylistic warnings as ...
Validation checks are run automatically whenever a page is saved “submitted” and can identify problems with a single variable, between two or more variables on the same eCRF page, or between variables on different pages. A variable can have multiple validation checks associated with it. Errors can be resolved in several ways:
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.
The Luhn algorithm or Luhn formula, also known as the "modulus 10" or "mod 10" algorithm, named after its creator, IBM scientist Hans Peter Luhn, is a simple check digit formula used to validate a variety of identification numbers.
Check digits and parity bits are special cases of checksums, appropriate for small blocks of data (such as Social Security numbers, bank account numbers, computer words, single bytes, etc.). Some error-correcting codes are based on special checksums which not only detect common errors but also allow the original data to be recovered in certain ...
The advantage of this method over repeated random sub-sampling (see below) is that all observations are used for both training and validation, and each observation is used for validation exactly once. 10-fold cross-validation is commonly used, [15] but in general k remains an unfixed parameter.