model evaluation

Model evaluation is the process of assessing how well a predictive model performs. It involves comparing the model’s predictions against actual outcomes to determine its accuracy and reliability. Various metrics, such as accuracy, precision, recall, and F1 score, help quantify its performance. This evaluation is crucial for understanding whether the model can be trusted to make decisions or predictions in real-world situations. A well-evaluated model ensures that users can have confidence in its results, which is especially important in fields like healthcare, finance, and technology.