Data synthesization can be optimized with regard to the quality requirement of the resulting synthetic data. Optimization is the task of finding a defined optimum. In the case of data synthesization, the main concern is the accuracy of the synthetic data, but in some cases it can also be the time required to obtain meaningful synthetic data. If the synthetic data is used for software testing and it is not necessary to preserve all the patterns present in the original data, a less sophisticated model can be used and its creation can be relatively fast. Conversely, once synthetic data needs to be used for model development or analysis, more complex models must be used, which can take longer to train.
Here you will find an overview of all cookies used. You can give your consent to whole categories or display further information and select certain cookies.