# Obtain the most important input data for binary classification on a neural network

I have a simple neural network for a binary classification.

Input features include: age, sex, economic_situation, illness, disability, etc.

Output is simply 1 and 0

I would like to order the features for each input from greatest to least impact it had on the classification.

An example answer could look like this:

Classification: 1
1) illness
2) economic_situation
3) disability
4) sex
5) age


Another example:

Classification: 0
1) economic_situation
2) age
3) disability
4) sex
5) illness


2) Look at the gradients magnitude $$|\nabla_f {y} |$$. You can either look at the raw gradient or look at the guided back-propagation which is just the back props product rule, but you only look at when the nodes positively help trigger a neuron by taking only the positive gradients at each step.