Let's select MLPClassifier.

In MLPClassifier there is loss_curve_ available. If there is early_stopping enabled then some part of the data is used as validation. Can we save the loss of training and validation data in the loss_curve_ as well?

Additional context

I've compared the MLP implementation with Tensorflow implementation and it works very well, there are no significant differences in the performance. You can read the comparison details at my blog post. I'm using the MLP in my AutoML mljar-supervised which creates Markdown reports for each model. I would like to have learning curves available in the report.