Stability of Random Forests and Coverage of Random-Forest Prediction Intervals
Authors: Yan Wang, Huaiqing Wu, Dan Nettleton
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that stability may persist even beyond our assumption and hold for heavy-tailed Y 2. and Numerically show that RF stability may hold beyond the above light-tail assumption; |
| Researcher Affiliation | Academia | Yan Wang Department of Mathematics Wayne State University Detroit, MI 48202 wangyan@wayne.edu Huaiqing Wu, Dan Nettleton Department of Statistics Iowa State University Ames, IA 50011 {isuhwu,dnett}@iastate.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. It mentions using the "randomForest" package in R, which is a third-party tool, but does not provide its own implementation code. |
| Open Datasets | No | We created a virtual dataset consisting of n 4000 points. We let Y be a standard Cauchy random variable, which is even without a well-defined mean. The feature vector X P R3 is determined as X r0.5Y sinp Y q, Y 2 0.2Y 3, It Y ą 0u ζs T where ζ is a standard normal random variable. The paper describes how the data was generated but does not provide access information (link, DOI, repository) for this virtual dataset. |
| Dataset Splits | Yes | We used 3000 of the points for training and 1000 of them as test points. |
| Hardware Specification | No | The paper only states: 'The computation can be done within a few minutes on a laptop.' This is not specific enough to identify the hardware. |
| Software Dependencies | No | The paper mentions using the "random Forest package in R" but does not provide specific version numbers for the software dependencies. |
| Experiment Setup | Yes | Using the random Forest package with default setting (except letting B 1000), we had an output RF predictor RFB. |