Partitioning Structure Learning for Segmented Linear Regression Trees
Authors: Xiangyu Zheng, Song Xi Chen
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 zhengxiangyu@pku.edu.cn Song Xi Chen Peking University csx@gsm.pku.edu.cn |
| 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. |