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.
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees
Authors: Klaus Broelemann, Gjergji Kasneci
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,413 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,940 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 | In this section, we will evaluate the effectiveness of this novel method on multiple datasets. The experiments cover both classification and regression. |
| Researcher Affiliation | Industry | Klaus Broelemann and Gjergji Kasneci SCHUFA Holding AG, Wiesbaden, Germany |
| Pseudocode | Yes | Algorithm 1 Training Model Trees... Algorithm 2 Gradient-Based Split Finding |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | All datasets come from public sources2 [Yeh and Lien, 2009; Zikeba et al., 2016] and cover both classification and regression tasks. The number of samples and attributes of these datasets are displayed in Tab. 3. |
| Dataset Splits | Yes | For our experiments, we performed 4-fold cross validation and averaged the 4 performance measurements. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | With transparency and shallow trees in mind, we restrict all model trees to a fixed depth of 1, 2, or 3. ... For our experiments, we performed 4-fold cross validation and averaged the 4 performance measurements. |