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I am working on a machine learning problem where I have to predict a set of $N$ numbers (proportions) for each data point, all of them summing to one. One toy example to illustrate my problem would be predicting at a daily level the percentage of volume of water rained in each of the states of the US over the total rain in the country - in this example $N=50$ (the number of states) and $\sum_{n=1}^{50}{\hat{y}_n}=1$

I was thinking on designing a neural net with $N$ outputs and apply a Softmax in the output, then backpropagate the MSE or the RMSE... I am a bit unsure about the convergence guarantees (potential vanishing gradient). I would also like to know if you would approach the problem in another way.

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    $\begingroup$ Depending on your independent variables, I should imagine that the better problem would be to build a model to predict the amount of rain in each area. If you then would like to have percentages, simply divide by the total predicted rain over all areas. $\endgroup$
    – Him
    Apr 1 '20 at 14:50
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    $\begingroup$ But that is very dangerous, given that only an outlier in one of the communities would bias all your distribution... I would rather like to adjust all the probabilities at the same time with the sum-1 constraint $\endgroup$
    – ivallesp
    Apr 1 '20 at 14:55
  • $\begingroup$ It may be worth noting that, at least for classification problems, NNs often need special care with calibration. I would think that this problem would be equally bad for regression on a compositional variable. Just something to think about if you end up going that route. $\endgroup$
    – Him
    Apr 1 '20 at 14:55
  • $\begingroup$ "an outlier in one of the communities would bias all your distribution" I'm not sure how constraining the model to sum to 1 would alleviate this. Would you care to elaborate? $\endgroup$
    – Him
    Apr 1 '20 at 14:56
  • $\begingroup$ As you increase $N$, the probability of having a very large value in one of the sub-models increases. This approach optimizes the different sub-models separately while the approach I am looking for would optimize a calibrated output (with a Softmax for example). Hence the model I am looking for should account for this risk of overestimation given that overestimating one of the outputs affects the prediction of all the outputs. $\endgroup$
    – ivallesp
    Apr 1 '20 at 15:07
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You have what is called . There is quite some literature on how to model this. Take a look through the tag, or search for the term.

Typically, one would choose a reference category and work with log ratios, or similar. One paper I personally know about predicting compositional data is Snyder at al. (2017, IJF). They use a state space approach, not an NN, but their transformation may still be useful to you.

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Answering my past self... One elegant solution is to use the cross-entropy with "soft-targets" as loss. This means that your targets will not be in one-hot-encodding format, but they will still sum to one. The original cross-entropy formula formula applies.

The cross-entropy loss with soft targets is widely used in the knowledge-distillation field: ref.

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