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..
Feature-Budgeted Random Forest
Authors: Feng Nan, Joseph Wang, Venkatesh Saligrama
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, on a number of benchmark datasets we demonstrate competitive accuracy-cost curves against state-of-the-art prediction-time algorithms. and 3. Experiments |
| Researcher Affiliation | Academia | Feng Nan EMAIL Joseph Wang EMAIL Venkatesh Saligrama EMAIL Boston University, 8 Saint Mary s Street, Boston, MA |
| Pseudocode | Yes | Algorithm 1 BUDGETRF 1: procedure BUDGETRF(F, B, C, ytr, Xtr, ytv, Xtv) ... Subroutine GREEDYTREE 8: procedure GREEDYTREE(F, C, y, X) |
| Open Source Code | No | The paper states, 'We use the code provided by the authors for Greedy Miser,' which refers to third-party code. There is no explicit statement or link indicating that the authors' own code for BUDGETRF is available. |
| Open Datasets | Yes | Yahoo! Learning to Rank: (Chapelle et al.) ... Mini Boo NE Particle Identification Data Set: (Frank & Asuncion) ... Forest Covertype Data Set: (Frank & Asuncion) ... CIFAR-10: (Krizhevsky, 2009) |
| Dataset Splits | Yes | There are 141, 397/146, 769/184, 968 examples in training/validation/test sets. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Matlab s Tree Bagger' and 'scikit-learn package' but does not specify version numbers for these or any other software dependencies, making the software environment not reproducible. |
| Experiment Setup | Yes | We run BUDGETRF using the threshold α = 0 for the threshold-Pairs impurity function. ... For each α we build a forest of maximum 40 trees using BUDGETRF. ... The optimization of classifiers in line 12 of Algorithm 1 is approximated by randomly generating 80, 40 and 20 stumps if the number of examples exceeds 2000, 500 and less than 500, respectively and select the best among them. |