Training a neural network with two groups of spacial coordinates per observation?
<p>I'm trying to predict an output (regression) where multiple groups have spacial (x,y) coordinates. I've been using scikit-learn's neural network packages (MLPClassifier and MLPRegressor), which I know can be trained with spacial data by inputting a 1-D array per observation (ex. the MNIST dataset). </p>

<p>I'm trying to figure out the best way to tell the model that group 1 has this set of spacial coordinates AND group 2 has a different set of spacial coordinates, and that combination yielded a result. Would it make more sense to input a single array where a group 1 location is represented by 1 and group 2 location is represented by -1? Or to create an array for group 1 and group to and append them? Still pretty new to neural nets - hopefully this question makes sense.</p>

<p>If I got this right, you are basically trying to implement classification variables into your input, and this is basically done by adding an input variable for each possible class (in your case "group 1" and "group 2") that holds binary values (1 if the sample belongs to the group, 0 if it doesn't). Wheather or not you would want to retain the actual coordinates depends on wheather you would like your network to process actual spatial data, or simply base it's output on the group that the sample belongs to. As I don't have much experience with the particular module you're using, I am unable to provide actual code, but I hope this helps.</p>

python machine-learning scikit-learn neural-network regression