Classification performance refers to the evaluation of how well a classification model predicts the categories or classes of data. In machine learning, classification is a supervised learning task where the goal is to assign labels to input data based on learned patterns from a training dataset. Assessing classification performance is crucial, as it helps determine the effectiveness of the model in making accurate predictions. Common metrics used to evaluate classification performance include accuracy, precision, recall, and the F1 score. Understanding these metrics allows practitioners to choose the most suitable model for their specific application and data characteristics.