Summation of Decision Trees
Ensembles of decision trees, like Random Forests are efficient machine learning models with state-of-the-art prediction quality. However, their predictions are much less transparent than those of a single decision tree. In this paper, we describe a prediction model based on a single decision tree in terms of Formal Concept Analysis. We define a differential way to describing a decision rule. We conclude by presenting an approach to summing an ensemble of decision trees into a single decision semilattice with the same predictions.