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 [1].
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). |