Control

In Data Science, control refers to the process of managing and regulating the variables in an experiment or study to ensure that the results are accurate and reliable. Control is an essential aspect of experimental design, as it allows researchers to isolate the effects of a specific variable and determine its impact on the outcome of the study. In machine learning, control can refer to the use of algorithms and techniques to regulate the behavior of a system or model, ensuring that it operates within certain parameters and produces consistent results. Control is also a critical component of data governance, as it involves establishing policies and procedures to ensure that data is accurate, secure, and compliant with relevant regulations and standards.
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