Let me preface this post with I am incredibly new to machine learning/neural networks. I am currently working on a classification neural network using TensorFlow whose input is multiple features of continuous data and whose output is an array of confidence levels for a group number (softmax). In some instances, the data passed into the model could have some undefined values in various rows. I understand from research and testing that input tensors' elements must all be of the same type. I have looked into a couple of options on how to fix the issue of these undefined values:
- I could simply set these undefined points equal to some constant like 0 or -1 (I believe this to be my best option, as it does not sacrifice other features)
- I could remove any row of data with an undefined value. I'm not a fan of this idea as I am working with high-dimensional data, so if I remove one row, my model would be missing out on quite a few columns worth of data.
Beyond these two, I have been unable to find any additional information.
I have tested both of these ideas, and while they fix the issue, they do have some negative impacts on the accuracy of my model. My question is this: What are some other effective ways of handling undefined values when working with neural networks?
I understand that the question is relatively vague, and I apologize if it has any necessary information missing. Please let me know if there is anything I can clarify.