Use trained non linear regression model to identify variables that maximize the predicted value I trained a non-linear regression model with 23 features. I tried to make sure the model doesn't overfit with ~0.6 r squared on validation data and with 0.75 correlation coefficient between the actual and model-predicted target values.
I would like to know if doing the following would make sense.

Use the model to identify values of the 23 features that would maximize the predicted value:

For example, consider a model to predict house price using: avg_area_price and sqfeet then identify value of avg_are_price and sqfeet that would result in the maximum house price. Note that we may not have observed this in the data but the regression model captured the input feature to output relationship so it could identify this.
I tried to look into it and found that linear programming could help doing this for linear regression. https://stats.stackexchange.com/questions/475639/can-we-use-linear-regression-to-define-the-objective-function-in-linear-programm
I did not find anything for finding the feature values that leads to the maximum predicted value for non-linear regression model.
Can anyone help with how to do this when we have non-linear regression model?
For non linear regression are you using SVM Kernel approach or deep learning?  One of the articles I have referred to in the past is this research paper from computational biology where in they have defined a measure for feature importance. I had come across this in the past when preparing for an interview so didnt actually try it out and see if it works.The link to the article :- [https://arxiv.org/pdf/1611.07567.pdf] . Let me know incase you do practically try this out