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..
HDI-Forest: Highest Density Interval Regression Forest
Authors: Lin Zhu, Jiaxing Lu, Yihong Chen
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark datasets show that HDI-Forest significantly outperforms previous approaches, reducing the average PI width by over 20% while achieving the same or better coverage probability. |
| Researcher Affiliation | Industry | Lin Zhu , Jiaxing Lu and Yihong Chen Ctrip Travel Network Technology Co., Limited. EMAIL |
| Pseudocode | Yes | Algorithm 1 Solve (23) for all 1 i en |
| Open Source Code | No | The paper does not provide an explicit statement or a link to the open-source code for HDI-Forest. |
| Open Datasets | Yes | We compare various methods on 11 datasets from the UCI repository4. Statistics of these datasets are presented in Table 1. 4http://archive.ics.uci.edu/ml/index.php |
| Dataset Splits | Yes | Each dataset is split in train and test sets according to a 80%-20% scheme, and we report the average performance over 10 random data splits. The hyper-parameters of all tested methods were tuned via 5-fold cross-validation on the training set. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Scikit-learn package' for QRGBDT and provides links to R packages for QRF and QR, and a GitHub link for QD-Ens, but does not provide specific version numbers for these software packages or for any other software used in their own implementation or experimental setup. |
| Experiment Setup | No | The paper states that 'The hyper-parameters of all tested methods were tuned via 5-fold cross-validation on the training set,' but it does not provide the specific hyperparameter values used for HDI-Forest or the baseline methods in the main text. |