In terms of data sets, what does the term 'holdout' mean?

The term 'holdout' refers to a specific subset of data that is set aside from the main dataset to evaluate the performance of a model or algorithm after it has been trained. This approach helps ensure that the model can generalize well to unseen data rather than just fitting to the training data.

Using a holdout data set allows for objective assessment of the model's predictive capability. Typically, following the training phase using other portions of the data, the holdout set is solely used for testing, which provides insights into how the model performs in real-world scenarios. This method aims to prevent overfitting, where a model performs exceptionally well on training data but fails on new, unseen data.

In contrast to testing, training datasets are used to teach the model; feature selection datasets are meant for determining the most relevant features in the data, and manipulation datasets pertain to alterations made to the data for specific processing or modeling purposes. Each of these plays a distinct role in the machine learning workflow, but the function of a 'holdout' dataset is uniquely tied to testing the efficacy of a trained model.