Model assessment is the process of evaluating how well a predictive model performs. This involves comparing the model's predictions against actual outcomes to determine its accuracy and reliability. Common metrics used in model assessment include accuracy, precision, recall, and F1 score. These metrics help identify strengths and weaknesses in the model, guiding improvements. Additionally, model assessment often includes techniques like cross-validation, which tests the model on different subsets of data to ensure it generalizes well to new information. By thoroughly assessing a model, data scientists can make informed decisions about its effectiveness and potential applications in real-world scenarios.