Authors: | Gossen, Frederik Steffen, Bernhard |
Title: | Algebraic aggregation of random forests |
Other Titles: | towards explainability and rapid evaluation |
Language (ISO): | en |
Abstract: | Random Forests are one of the most popular classifiers in machine learning. The larger they are, the more precise the outcome of their predictions. However, this comes at a cost: it is increasingly difficult to understand why a Random Forest made a specific choice, and its running time for classification grows linearly with the size (number of trees). In this paper, we propose a method to aggregate large Random Forests into a single, semantically equivalent decision diagram which has the following two effects: (1) minimal, sufficient explanations for Random Forest-based classifications can be obtained by means of a simple three step reduction, and (2) the running time is radically improved. In fact, our experiments on various popular datasets show speed-ups of several orders of magnitude, while, at the same time, also significantly reducing the size of the required data structure. |
Subject Headings: | Random forest Algebraic decision diagram Aggregation Explainability Interpretability Running time optimisation Memory optimisation |
Subject Headings (RSWK): | Entscheidungsgraph Aggregation Laufzeit Erklärung Klassifikator <Informatik> Speicher <Informatik> Optimierung |
URI: | http://hdl.handle.net/2003/40778 http://dx.doi.org/10.17877/DE290R-22635 |
Issue Date: | 2021-09-29 |
Rights link: | http://creativecommons.org/licenses/by/4.0/ |
Appears in Collections: | LS 14 Software Engineering |
Files in This Item:
File | Description | Size | Format | |
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Gossen-Steffen2021_Article_AlgebraicAggregationOfRandomFo.pdf | 1.53 MB | Adobe PDF | View/Open |
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