Scalable Bayesian Rule Lists
Authors: Hongyu Yang, Cynthia Rudin, Margo Seltzer
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through a series of controlled experiments, we show that SBRL is over two orders of magnitude faster than the previous best code for this problem. |
| Researcher Affiliation | Academia | 1Massachusetts Institute of Technology, Cambridge, Massachusetts, USA 2Duke University, Durham, North Carolina, USA 3Harvard University, Cambridge, Massachusetts, USA. |
| Pseudocode | Yes | Algorithm 1 Calculating bj s |
| Open Source Code | Yes | Code for SBRL is available at the following link: https://github.com/Hongyuy/sbrlmod Link to R package SBRL on CRAN: https://cran.r-project.org/web/packages/sbrl/ index.html |
| Open Datasets | Yes | We benchmark using publicly available datasets (see Bache & Lichman, 2013) |
| Dataset Splits | Yes | Evaluations of prediction quality, sparsity, and timing were done using 10-fold cross validation. |
| Hardware Specification | No | The paper mentions that experiments were run 'on a laptop' but does not provide specific hardware details such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper mentions 'python implementation', 'python gmpy library', 'Python to C', and 'GMP library' but does not provide specific version numbers for any of these software components. |
| Experiment Setup | Yes | The prior parameters were fixed at η = 1, and α = (1, 1). |