I understand how to make predictions with a trained neural network model that uses
loss=binary_crossentropy and a 1-node
activation=sigmoid output layer to make binary classifications.
But how can I determine the strength of association between a feature and a label? I'm trying to make a neural network that competes with generalized linear models that show
p values for each feature.
The predictions are not the important part. I need to provide insight about the features so that we can learn about the biology of a disease (DNA variant is feature and disease is label). I know that there is such a thing as feature importance, but isn't that just a rank ordered list of features?
def create_baseline(): model = Sequential() model.add(Dense(60, input_dim=60, kernel_initializer='normal', activation='relu')) model.add(Dense(1, kernel_initializer='normal', activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) return model