Model evaluation is the process of carefully checking how well a model performs after it has been trained It helps us understand how close the model’s predictions are to the actual values by comparing them using different measurements such as error rate accuracy and absolute error Through model evaluation we can find out whether the model is making small mistakes or large ones and how often it gives correct results This process also helps in identifying problems like over prediction or under prediction so that the model can be improved A strong model evaluation makes sure the model is reliable fair and useful before it is used in real world situations such as price prediction medical diagnosis or weather forecasting.