Abstract: A computer-implemented method for compensating for an uneven distribution in training data during the training of a machine learning algorithm. The training data include a plurality of data sets. The machine learning algorithm solves a regression task. The training data have an uneven distribution with regard to their labels. The method includes: defining auxiliary classes for the training data; creating a classification task; ascertaining a classification probability for each auxiliary class; ascertaining a classification loss function for the classification task; weighting the classification loss function; ascertaining an overall loss function; training the machine learning algorithm’ and providing the trained machine learning algorithm.

Abstract: The subject of the invention is a free-flowing dispersion containing at least one particulate metal oxide, metal oxide hydrate and/or metal hydroxide as a suspended solid, a dispersant and an organic, liquid dispersion medium. The invention also relates to the use of said dispersant.