# How to fit integer output in neural network

I have a data set that contains 104 input features and 96 output values ​​to be predicted, the input features are floating values ​​normalized to [-1,1], and each out put value would be one integer among [-1,0,1].

I am basically new to data science and machine learning, and I need to know what kind of neural network model would fit best for this kind of data. I have googled this for a quite long time, can you guys help me ? Thank you!

You may check a multitarget model like this one. Basically Keras‘ functional API gives you a lot of flexibility to model multitarget problems.

You have three classes per target, so make sure you use 'categorical_crossentropy' as loss function with three terminal layers.

If the output range is just three integers (-1, 0 and 1), the problem would probably be best approached as multinomial classification. For a start, you can look up a few multinomial classification tutorials for scikit-learn for Python: https://scikit-learn.org/stable/index.html.