Variable Importance Using Decision Trees
Authors: Jalil Kazemitabar, Arash Amini, Adam Bloniarz, Ameet S. Talwalkar
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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 sjalilk@ucla.edu Arash A. Amini UCLA aaamini@ucla.edu Adam Bloniarz UC Berkeley adam@stat.berkeley.edu Now at Google Ameet Talwalkar CMU talwalkar@cmu.edu |
| 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. |