Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Variable Importance Using Decision Trees
Authors: Jalil Kazemitabar, Arash Amini, Adam Bloniarz, Ameet S. Talwalkar
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further demonstrate the effectiveness of these impurity-based methods via an extensive set of simulations. |
| Researcher Affiliation | Collaboration | S. Jalil Kazemitabar UCLA EMAIL Arash A. Amini UCLA EMAIL Adam Bloniarz UC Berkeley EMAIL Now at Google Ameet Talwalkar CMU EMAIL |
| Pseudocode | Yes | Algorithm 1 DSTUMP |
| Open Source Code | No | The paper does not provide any specific links to open-source code or explicitly state that code is available. |
| Open Datasets | No | The paper states: 'We generate the training data as X = e XM where e X Rn p is a random matrix with IID Unif( 1, 1) entries'. The data is generated, not sourced from a public dataset with an access link or citation. |
| Dataset Splits | No | The paper describes generating its own data but does not specify any training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models or types of machines used for running experiments. |
| Software Dependencies | No | The paper does not specify any software names with version numbers that would be necessary to replicate the experiment. |
| Experiment Setup | Yes | We fix p = 200, σ = 0.1, and let βi = 1/ s over its support i S, where |S| = s. |