Model performance refers to how well a machine learning model makes predictions or classifications based on input data. It is typically evaluated using metrics such as accuracy, precision, recall, and F1 score. These metrics help determine how effectively the model can generalize its learning to new, unseen data. To assess model performance, data scientists often use a separate test dataset that was not included during the training phase. This ensures that the evaluation reflects the model's ability to perform in real-world scenarios. Continuous monitoring and improvement of model performance are essential for maintaining its effectiveness over time.