Description: Data randomization is the process of randomly rearranging the order of samples in a dataset. This procedure is fundamental in the fields of machine learning and statistics, as it helps eliminate biases in the data and ensures that trained models are more robust and generalizable. By mixing the data, it prevents the model from learning spurious patterns that may be present in the original order of the samples. Randomization is especially important in hyperparameter optimization, where the goal is to find the best configuration for a model. By conducting multiple experiments with different combinations of hyperparameters, randomization ensures that each dataset used in validation is representative and not influenced by the order in which the samples were presented. This contributes to a more accurate assessment of model performance and better hyperparameter selection. In summary, data randomization is an essential technique that enhances the quality of machine learning by providing a more solid foundation for model evaluation and optimization.
