Hi
In the context of neural networks within MATLAB, the training regression plot serves as a valuable tool for evaluating the correspondence between the network's outputs and the target values. For a comprehensive understanding, you may refer to the official documentation available at
In this context, a regression coefficient ( R = 1 ) signifies a perfect fit, indicating that the network's outputs precisely match the target values. This suggests exceptional model performance. A high ( R ) value reflects that the model's predictions are in close alignment with the target values. When ( R = 1 ), the model demonstrates optimal performance on the training data. It is advisable to validate the model using test data to ensure robust generalization across different datasets.
