  Random Forests and related tree-based methods are popular for supervised
learning from table based data. Apart from their ease of parallelization, their
classification performance is also superior. However, this performance,
especially parallelizability, is offset by the loss of explainability.
Statistical methods are often used to compensate for this disadvantage. Yet,
their ability for local explanations, and in particular for global
explanations, is limited. In the present work we propose an algebraic method,
rooted in lattice theory, for the (global) explanation of tree ensembles. In
detail, we introduce two novel conceptual views on tree ensemble classifiers
and demonstrate their explanatory capabilities on Random Forests that were
trained with standard parameters.
