if my understanding is correct, in case of image classification and NLP, if I have a pre-trained model, to train on new data, I can reshape the data according to the pre-trained model. So there is no problem even if the new data is slightly different from the previous data. I am trying to use transfer learning for a regression problem. Consider I train a base model with 15 parameters and 1 million rows. I train a model. Now if I want to use this model for a similar problem case but I have only 14 parameters, one parameter is missing. Will the pre-trained model be of any use. Is there a way I can use transfer learning in such cases?