Holdout testing is a method used to evaluate the performance of a machine learning model by reserving a portion of the dataset for testing, while the remaining data is utilized for training the model. This technique helps ensure that the model's performance is assessed on unseen data, providing a more accurate measure of its generalization ability. In hybrid intelligent decision-making systems, holdout testing plays a crucial role in validating models that integrate various computational intelligence techniques, ensuring they can effectively handle real-world scenarios.
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