After training a neural network, it is important to check how well it actually performs. This step is called evaluation and validation. It ensures that the model is not just memorizing training data but is truly learning patterns that work on new, unseen inputs. There are three key aspects to understand here: metrics, benchmarking, and robustness testing.

Metrics are the measurements we use to judge the performance of a model. They are like scores that tell us how good or bad the predictions are. Different tasks use different metrics. For example, if the task is to decide between categories, accuracy (the fraction of correct predictions) is often used. In cases where accuracy alone is not enough, other metrics like precision, recall, or F1-score are used, which give a more detailed view of the model’s strengths and weaknesses. Metrics provide a clear numerical way to evaluate the model’s success.

Benchmarking means comparing the performance of a model against a standard point of reference. Instead of just looking at raw scores, benchmarking places a model in context. This can involve comparing it to earlier models, to a simple baseline method, or to established public results on a common dataset. Benchmarking helps determine whether a new model is actually an improvement or not. It provides a fair way to judge progress by checking how a model performs relative to others under the same conditions.

Robustness testing focuses on how reliable the model is under different or challenging conditions. A model might perform well on typical data but fail when the data changes slightly. Robustness testing checks whether the network can handle noise, small changes, or unusual cases without breaking down. This is important because we want the model to be stable and dependable, not just accurate on one specific dataset. By testing robustness, we gain confidence that the neural network can generalize beyond its training environment.

https://link.springer.com/article/10.1007/s10994-021-05994-9?utm_source=chatgpt.com

https://www.geeksforgeeks.org/machine-learning/metrics-for-machine-learning-model/

https://www.geeksforgeeks.org/business-studies/benchmarking-steps-and-types/

https://www.geeksforgeeks.org/software-testing/robustness-testing/