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..
CatBoost: unbiased boosting with categorical features
Authors: Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate that Cat Boost outperforms leading GBDT packages and leads to new state-of-the-art results on common benchmarks. |
| Researcher Affiliation | Collaboration | 1Yandex, Moscow, Russia 2Moscow Institute of Physics and Technology, Dolgoprudny, Russia |
| Pseudocode | Yes | Algorithm 1: Ordered boosting" and "Algorithm 2: Building a tree in Cat Boost |
| Open Source Code | Yes | Their combination is implemented as an open-source library1 called Cat Boost (for Categorical Boosting ), which outperforms the existing state-of-the-art implementations of gradient boosted decision trees XGBoost [8] and Light GBM [16] on a diverse set of popular machine learning tasks (see Section 6). 1https://github.com/catboost/catboost |
| Open Datasets | Yes | We compare our algorithm with the most popular open-source libraries XGBoost and Light GBM on several well-known machine learning tasks. The detailed description of the experimental setup together with dataset descriptions is available in the supplementary material (Section D). |
| Dataset Splits | No | The parameter tuning and training were performed on 4/5 of the data and the testing was performed on the remaining 1/5. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments in the main text. |
| Software Dependencies | No | The paper mentions using 'XGBoost' and 'Light GBM' but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | No | The detailed description of the experimental setup together with dataset descriptions is available in the supplementary material (Section D). |