Constraints
Constraints are rules or limitations that are applied to data in order to ensure its accuracy, consistency, and validity. In data science, constraints are used to define the boundaries of a problem and to ensure that the solution meets certain requirements. Constraints can be applied to data at various stages of the data science process, from data collection and cleaning to modeling and analysis. Examples of constraints include data type constraints, range constraints, and uniqueness constraints. Data type constraints specify the type of data that can be stored in a particular field, while range constraints specify the allowable range of values for a field. Uniqueness constraints ensure that each record in a dataset is unique. Constraints can be enforced through various means, such as database triggers, data validation rules, and machine learning algorithms.
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