Feature importance in neural networks Hello I am using keras to develop a neural network model and I have a data of 45 numerical predictor variables, 2 categorical targets that will be predicted each with a different model. As I found, there is no feature importance model in keras. There are three options I can use, correlation ratio between the variables, kendals rank coefficient values and lasso regulation. Which one do you think is suitable to be used for feature selection in neural networks?
For feature importance, you might want to consider using Shapley values or LIME. There are some examples here.