Neural network for Multiple integer output I have a data set that contains 135 input features and 132 output values to be predicted. The input features are all numeric floating point values and each output value would be an integer between [0,1,2,3,4].

I am basically new to data science and machine learning, therefore I need to understand what kind of neural network model (regression or classification) would fit best for this kind of data. On one hand the output values represent different classes but I am not sure how a single neural network can predict multiple classes from the same input data.
welcome to the site!

I think the key word you need to know that defines your task is: multi-target classification or regression.

You can find an explanation and some possible techniques at this link.

For neural networks:

The key is to remember that the last layer should have linear activations (i.e. no activation at all).

As per your requirements, the shape of the input layer would be a vector (135,) and the output (132,).

The usual loss function used for regression problems is mean squared error (MSE). Here's an example of multidimensional regression using Keras:

model = Sequential()
model.add(Dense(200, input_dim = (135,)))
model.add(Activation('relu'))
model.add(Dense(200))
model.add(Activation('relu'))
model.add(Dropout(0.3))
model.add(Dense(132))
model.compile(loss='mean_absolute_error', optimizer='Adam')