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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]
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]
Usually such patterns are used by string-searching algorithms for "find" or "find and replace" operations on strings, or for input validation. Regular expression techniques are developed in theoretical computer science and formal language theory.
In Python 2 (and most other programming languages), unless explicitly requested, x / y performed integer division, returning a float only if either input was a float. However, because Python is a dynamically-typed language, it was not always possible to tell which operation was being performed, which often led to subtle bugs, thus prompting the ...
For example, Python adds the feature that hash functions make use of a randomized seed that is generated once when the Python process starts in addition to the input to be hashed. [9] The Python hash is still a valid hash function when used within a single run, but if the values are persisted (for example, written to disk), they can no longer ...
User input validation: User input (gathered by any peripheral such as a keyboard, bio-metric sensor, etc.) is validated by checking if the input provided by the software operators or users meets the domain rules and constraints (such as data type, range, and format).
A simple example is a test-suite—the input/output pairs specify the functionality of the program. A variety of techniques are employed, most notably using satisfiability modulo theories (SMT) solvers, and genetic programming , [ 7 ] using evolutionary computing to generate and evaluate possible candidates for fixes.
Example 1: legacy code may have been designed for ASCII input but now the input is UTF-8. Example 2 : legacy code may have been compiled and tested on 32-bit architectures, but when compiled on 64-bit architectures, new arithmetic problems may occur (e.g., invalid signedness tests, invalid type casts, etc.).