Can anyone interpret this regression plot for neural networks?

Hello! I am trying a multilayer perceptron for intradaily data. The training is going smoothly but I get the following regression plot. Can anyone tell me if that is acceptable result? Thank you!!

Answer 1

[Nipun Katyal]

As the ylabel of these graphs denote an equation between the predicted value and the target value, with output as the dependent variable and target as the independent variable. These equations can be used to show how well your MLP is able to perform. The coefficient of Target shows the proportionality between the output and the targets, hence for a good performance MLP it should be as close to unity as possible. The second term which is a constant, is the error or the residue which should be added to the scaled Target to make it as close as possible to the predicted output, ideally it should be zero or as small as possible. The title represents the coefficient of regression between target and the output. As far as the results for your classifier go, there is some disparity between the training and the testing accuracy, maybe it is because of overfitting, but now you have a clear idea about the plots and can use them to compare the results to find the best results.

[kareema jumoorty]

Thank you for your answer. I've included the performance plot of my neural network and there does not seem to be any significant overfitting. So I am not able to figure out what is wrong. I've varied the number of hidden nodes and found the one yielding the lowest testing error and these are the results.

[Nipun Katyal]

Apart from overfitting hyperparameters the two main causes can be:

1. The size of the test set is too small.
2. The test and the train data follow different distributions which explains the difference in the accuracies at the time of validation and testing.

[kareema jumoorty]

Thank you for your answers!!!

Answer 2

[Jesse Nyokabi]

Going through the same now! The answers are okay