How is the performance of a model evaluated?

The performance of a model is evaluated primarily by comparing it against a test dataset. This approach ensures that the model is assessed on new data that it has not encountered during its training phase. The test dataset allows for a clear evaluation of how well the model generalizes its learning to unseen examples, which is crucial for understanding its effectiveness in real-world applications.

Using a test dataset helps to gauge various performance metrics, such as accuracy, precision, recall, and F1 score, among others. This assessment is vital for validating the model's predictive capabilities and ensures that it is not just memorizing the training data but instead learning patterns that can be applied to new inputs.

While other factors, such as computational speed, may be important in practical applications, they do not directly measure the quality of the model's predictions. Using only the training dataset would not provide insight into how well the model performs in general since its performance could be overly optimistic if it only reflects the data it was trained on. Similarly, using random number inputs does not relate to meaningful evaluation as it does not reflect the model's understanding of the actual problem domain.