I am currently using tensorflow to create a neural network that does basic binary classification, and I would like to examine the bias of the model after training.
I have a dataset of about 300,000 samples, and each sample has a list of 112 features. Prior to training, we've selected 20 features that we want to use for prediction - race is a feature in the dataset, but it is not used for training the neural network.
What I'd like is to be able to see how often a variable with a different values for "race" gets misclassified, without including the race feature in the training dataset. Is there a way to essentially drop that feature during training, then re-append it to the dataset to see what the accuracy is on specific groups? Or to tell tensorflow to ignore that variable while training?