neural network with multiple outputs in sklearn

Question

I'm trying to build a neural network to predict the probability of each tennis player winning a service point when they play against each other. For inputs I would use last N matches that each player played, taking the ranking difference against his opponent and the actual probability of winning a point he had in the match.

For example, looking at only 2 matches for each player, one input would be

i=[-61, 25, 0.62, 0.64, 2, -35, 0.7, 0.65]

First 4 numbers are for 1st player (ranking differences and probabilities he had), other 4 for second. Output would be

o=[0.65, 0.63]

So training inputs would be X=[i1, i2, i3,...] and outputs y=[o1, o2, o3,...]

I have a couple of newbie questions:

1. is it necessary to normalize inputs (ranks and probabilities respectively) across the entire dataset?
2. when I try to run this in python it says

ValueError: Multioutput target data is not supported with label binarization

Can I make MLPClassifier work with 2 outputs?

EDIT: added some code

from sklearn.neural_network import MLPClassifier
clf = MLPClassifier(solver='lbfgs', alpha=1e-5,
                   hidden_layer_sizes=(5, 2), random_state=1)
X=[[-61, 25, 0.62, 0.64, 2, -35, 0.7, 0.65], [2,-5,0.58,0.7,-3,-15,0.65,0.52] ]
y=[ [0.63, 0.64], [0.58,0.61] ]
clf.fit(X,y)

that code return the mentioned error. data isn't normalized here, but let's ignore that for now.

Answer 1

Your first question is answered here in detail: Why do we have to normalize the input for an artificial neural network? In short, yes, just normalize the values, it makes life easier.

The 2nd question is covered here:

MLPClassifier supports multi-class classification by applying Softmax as the output function.

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If you can add some of your code to the question, the answer could be more detailed.

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Edit

After reading the question again, more carefully, I realized that you are trying to use a classifier function, i.e. you are trying to apply labels to your input data. This means that the function is expecting binary output.

You are probably looking for a Multi-layer Perceptron regressor which will give continuous output values.

from sklearn.neural_network import MLPRegressor
clf = MLPRegressor(solver='lbfgs', alpha=1e-5,
                   hidden_layer_sizes=(5, 2), random_state=1)
X=[[-61, 25, 0.62, 0.64, 2, -35, 0.7, 0.65], [2,-5,0.58,0.7,-3,-15,0.65,0.52] ]
y=[ [0.63, 0.64], [0.58,0.61] ]
clf.fit(X,y)

MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
       beta_2=0.999, early_stopping=False, epsilon=1e-08,
       hidden_layer_sizes=(5, 2), learning_rate='constant',
       learning_rate_init=0.001, max_iter=200, momentum=0.9,
       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,
       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,
       warm_start=False)