Feature-Budgeted Random Forest
Authors: Feng Nan, Joseph Wang, Venkatesh Saligrama
ICML 2015 | Conference PDF | Archive PDF | Plain Text | 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 FNAN@BU.EDU Joseph Wang JOEWANG@BU.EDU Venkatesh Saligrama SRV@BU.EDU 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. |