Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
Authors: Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,434 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,851 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on multiple public datasets show that, Light GBM speeds up the training process of conventional GBDT by up to over 20 times while achieving almost the same accuracy.In this section, we report the experimental results regarding our proposed Light GBM algorithm. We use five different datasets which are all publicly available. |
| Researcher Affiliation | Collaboration | 1Microsoft Research 2Peking University 3 Microsoft Redmond |
| Pseudocode | Yes | Algorithm 1: Histogram-based Algorithm; Algorithm 2: Gradient-based One-Side Sampling; Algorithm 3: Greedy Bundling; Algorithm 4: Merge Exclusive Features |
| Open Source Code | Yes | The code is available at Git Hub: https://github.com/Microsoft/Light GBM. |
| Open Datasets | Yes | We use five different datasets which are all publicly available. The details of these datasets are listed in Table 1. Among them, the Microsoft Learning to Rank (LETOR) [26] dataset contains 30K web search queries. The features used in this dataset are mostly dense numerical features. The Allstate Insurance Claim [27] and the Flight Delay [28] datasets both contain a lot of one-hot coding features. And the last two datasets are from KDD CUP 2010 and KDD CUP 2012. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology for train/validation/test) was found. While it mentions 'All algorithms are run for fixed iterations, and we get the accuracy results from the iteration with the best score,' implying some form of validation, the validation split itself is not explicitly defined. |
| Hardware Specification | Yes | Our experimental environment is a Linux server with two E5-2670 v3 CPUs (in total 24 cores) and 256GB memories. All experiments run with multi-threading and the number of threads is fixed to 16. |
| Software Dependencies | No | The paper mentions software like XGBoost, scikit-learn, and gbm in R, but does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | Yes | We set a = 0.05, b = 0.05 for Allstate, KDD10 and KDD12, and set a = 0.1, b = 0.1 for Flight Delay and LETOR. We set γ = 0 in EFB. All algorithms are run for fixed iterations, and we get the accuracy results from the iteration with the best score. |