1. Transformation

Transformation of models are at the core of probly. They allow you to turn your machine learning model into a uncertainty-aware version of itself. This allows probly to be used post-hoc, meaning after you have already created (or even trained) your model in whatever way you like.

Examples

• Applying the Dropout Transformation