SIRUS: making random forests interpretable

By Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet. 2019

10 citations recorded by the source corpus.

State-of-the-art learning algorithms, such as random forests or neural networks, are often qualified as "black-boxes" because of the high number and complexity of operations involved in their prediction mechanism. This lack of interpretability is a strong limitation for applications involving critical decisions, typically the analysis of production processes in the manufacturing industry. In such critical contexts, models have to be interpretable, i.e., simple, stable, and predictive. To address this issue, we design SIRUS (Stable and Interpretable RUle Set), a new classification algorithm based on random forests, which takes the form of a short list of rules. While simple models are usually unstable with respect to data perturbation, SIRUS achieves a remarkable stability improvement over cutting-edge methods. Furthermore, SIRUS inherits a predictive accuracy close to random forests, combined with the simplicity of decision trees. These properties are assessed both from a theoretical and empirical point of view, through extensive numerical experiments based on our R/C++ software implementation sirus available from CRAN.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Mathematical optimization in classification and regression trees (2021, methodology): “Alternatively, one can extract a collection of rules, and techniques such as column generation may be used as in Birbil et al. (2020) or heuristics as in Bénard et al. (2019, 2020).”
Interpretable Random Forests via Rule Extraction (2020, methodology): “Since the output Y is bounded (by Assumption (A3)), the consistency of ˆ p M,n ( P ) can be easily adapted from Theorem 1 of B´enard et al. (2019).”