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..
Enumerating Distinct Decision Trees
Authors: Salvatore Ruggieri
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. ExperimentsTable 1 reports the number of instances and of features for small and large standard benchmarks datasets publicly available from (Lichman, 2013).Fig. 3 shows, for the IG split criterion, the distribution of distinct decision trees w.r.t. the size of attribute subset. |
| Researcher Affiliation | Academia | 1University of Pisa and ISTI-CNR, Pisa, Italy. |
| Pseudocode | Yes | Algorithm 1 subset(R, S) enumerates R 1 Pow(S), Algorithm 2 DTdistinct(R, S) enumerates distinct decision trees necessarily using R and possibly using S as split features |
| Open Source Code | Yes | The extended Ya DT version is publicly downloadable from: http://pages.di.unipi.it/ruggieri. |
| Open Datasets | Yes | Table 1 reports the number of instances and of features for small and large standard benchmarks datasets publicly available from (Lichman, 2013). |
| Dataset Splits | Yes | Following (Reunanen, 2003), we adopt 5-repeated stratified 10-fold cross validation in experimenting with wrapper models. For each holdout fold, feature selection is performed by splitting the 9fold training set into 70% building set and 30% search set using stratified random sampling. |
| Hardware Specification | Yes | Test were performed on a commodity PC with Intel 4 cores i52410@2.30 GHz, 16 Gb RAM, and Windows 10 OS. |
| Software Dependencies | No | The paper mentions implementation in C++ using the Ya DT system, but does not provide specific version numbers for the compiler, Ya DT system, or any other software dependencies. |
| Experiment Setup | Yes | Information Gain (IG) is used as quality measure in node splitting. No form of tree simplification (e.g., error-based pruning) is used. The m parameter is set to the small value 2 for all datasets. |