Name
Abstract | ||
|---|---|---|
In this paper, we present Forest GUMP (for Generalized, Unifying Merge Process) a tool for providing tangible experience with three concepts of explanation. Besides the well-known model explanation and outcome explanation, Forest GUMP also supports class characterization, i.e., the precise characterization of all samples with the same classification. Key technology to achieve these results is algebraic aggregation, i.e., the transformation of a Random Forest into a semantically equivalent, concise white-box representation in terms of Algebraic Decision Diagrams (ADDs). The paper sketches the method and illustrates the use of Forest GUMP along an illustrative example taken from the literature. This way readers should acquire an intuition about the tool, and the way how it should be used to increase the understanding not only of the considered dataset, but also of the character of Random Forests and the ADD technology, here enriched to comprise infeasible path elimination. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-030-99527-0_17 | TOOLS AND ALGORITHMS FOR THE CONSTRUCTION AND ANALYSIS OF SYSTEMS, TACAS 2022, PT II |
Keywords | DocType | Volume |
Random Forest, Binary/Algebraic Decision Diagram, Aggregation, Infeasible Paths, Explainability, Random Seed | Conference | 13244 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alnis Murtovi | 1 | 1 | 1.71 |
| Alexander Bainczyk | 2 | 1 | 0.68 |
| Bernhard Steffen | 3 | 0 | 0.34 |