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..
Partitioning Structure Learning for Segmented Linear Regression Trees
Authors: Xiangyu Zheng, Song Xi Chen
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The practical performance of the SLRT and its ensemble versions are evaluated via numerical simulations and empirical studies. The latter shows their advantageous predictive performance over a set of state-of-the-art tree-based models on well-studied public datasets. |
| Researcher Affiliation | Academia | Xiangyu Zheng Peking University EMAIL Song Xi Chen Peking University EMAIL |
| Pseudocode | Yes | Algorithm 1 Recursive Partitioning for Conditional Uncorrelated Regressors; Algorithm 2 Split Selection for Correlated Regressors |
| Open Source Code | Yes | The source code of the algorithm is available in the supplementary material. |
| Open Datasets | Yes | The predictive performance is tested on 9 benchmark datasets from the Stat Lib library [23] and the UCI Machine Learning Repository [24] |
| Dataset Splits | Yes | Then, the optimally pruned subtree is T(α ). Let b L be the number of terminal nodes in T(α ), under certain general conditions for the distribution of ε and given appropriate α , it can be proved that b L converges to the genuine number of segments L0 in probability. ... The optimal complexity parameter α is selected from { αk}K k=1 by the ten-fold cross-validation to optimize the average predictive accuracy measured by the sum of squared residuals. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions general software or methods (e.g., 'LASSO procedure', 'random forests', 'CART') but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | With the same stopping parameter of Nmin = 10, Depmax = 10, we applied SLRT and CART respectively, obtaining the approximated surface in Figure 4 and 5. |