I have a dataset with approx 6 input features and 5 output values to be predicted. I am trying to understand what kind of neural network would be most suitable to assign probability across multiple dependent outputs, with the motivation to maximise the value of a single feature.
For example, suppose the inputs are:
company size number of employees turnover average salary country years of operation
And the outputs to be predicted are:
% budget allocated to marketing % budget allocated to sales % budget allocated to R&D % budget allocated to training % budget allocated to shareholders
The training dataset also contains the column:
Which is what I want to optimise against (i.e. the primary motivation of the model).
For a given set of inputs (company size, number of employees, turnover, average salary, country, years of operation), I want to be able to predict values for the 5 outputs which are most likely to achieve the highest 'profit'. The sum of the 5 outputs must equal 100%.
In other words, there is a finite budget to be allocated, and I want to create a model to predict the best budget allocation to maximise profit.
What neural network would be most suitable for this purpose? I have looked into multi-output regression, however I imagine this would assign independent probabilities for each of the 5 outputs (i.e. they won't add up to 100%).
Is it possible for probability to instead be assined to each output dependent of the other outputs (i.e. so they add up to 100%)? If so, is there a name for this type of approach?