Allow advanced users to choose custom transformation functions on raw prediction scores

[tlarcher]

• Context
  Malpolon is built for both beginners and advanced users in machine & deep learning. In such, the framework allows (or will allow) selecting a task among several available (binary classification, multi-label classification, abundance prediction [regression]...) with default parameters and transparent (but parametrable) use of loss functions.

• Problem
  Depending on the use case, one advanced user might want to work with the raw scores of the model before passing them on to the last activation function, which is sometimes bound with the loss function.

Currently this is not possible and we would need to find an elegant way to implement this possibility without adding layers of complexity for beginner users.

• Solution
  Most likely, model_builder.py and standard_prediction_systems.py will have to be re-structured to allow this new parametrization.
  This new parameter could very well be read from the .yaml config files but seeing that this new feature would only be used by advanced users, it remains to be decided weither to complexify files directly manipulated by beginners or simply embed the code with the possibility of extracting the raw scores layer.