I have a non linear dataset with around 25 features and 2 target variables to be predicted. The data points have a wide spread. It is a regression problem. The attached image shows the non-linear distribution of the data which shows how the data is dispersed (Target1 vs each feature).
I have 2 questions:
I want to know if CNN networks with non-linear activation layers can be used to predict multiple target variables in such non-linear patterns?
Do I need to use any specific CNN architecture (encoder-decoder may be) to handle non-linear regression with dispersed pattern as in the pairplot image?
Hi Shilpa! I am running into a problem with non-linear regression and I would like to know if you get a solution for this problem you posted some months ago. Could you manage to solve this problem?
I tried both a sequential network and a CNN network. The sequential network worked better in my case. CNN did not reduce loss beyond a point even after multiple hyper-parameter adjustments. What is the error/issue that you are facing?
Hi Shilpa, thanks for the answer!
Okay, is good to know that the sequential network worked better than the CNN. My problem is different from yours, I think. I explained it in the following link https://discuss.pytorch.org/t/getting-a-zero-grad-in-non-linear-regression/140005
Check this blog, if it helps.
In your other post, what is happening in the “class” function? Can you try with a sequential network with multiple hidden layers with non-linear activations?
Thank you for the post you shared! I answered your questions in the other post.