Implement _randomized classifier_ predictions

[AndreFCruz]

Summary

Using a FairGBM model as a randomized classifier is described in detail in the FairGBM paper. However, this library only allows the use of the last FairGBM iterate --- this should achieve similar performance with faster predictions, but it would be interesting to still be able to use the randomized classifier predictions for comparison and future research.

Description

These randomized classifier predictions are simply generated by matching each input row with a random boosting iterate, and using that iterate to generate the row's predictions (selected at random with replacement).

This could even be done only on the Python package part, by adding a new method to the FairGBMClassifier class, named predict_randomized predict_proba_randomized, or adding a new flag randomized=True to the existing predict and predict_proba methods.

References

https://arxiv.org/pdf/2209.07850v2.pdf