A test set is a subset of data used to evaluate the performance of a machine learning model after it has been trained on a training set. This evaluation is crucial as it helps in assessing how well the model can generalize its predictions to unseen data, which is important for ensuring accuracy in practical applications. The quality of the test set can significantly influence the reliability of performance metrics, thus playing a key role in model selection and tuning.
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