Connecting Interpretability and Robustness in Decision Trees through Separation
Authors: Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in Section 6, we test the validity of the separability assumption and the quality of the new algorithm on real-world datasets that were used previously in tree-based explanation research. |
| Researcher Affiliation | Academia | 1University of California, San Diego. Correspondence to: Michal Moshkovitz <mmoshkovitz@eng.ucsd.edu>, Yao-Yuan Yang <yay005@eng.ucsd.edu>. |
| Pseudocode | Yes | Algorithm 1 BBM-RS (BBM-Risk Score) |
| Open Source Code | Yes | The code for the experiments is available at https://github.com/yangarbiter/ interpretable-robust-trees. |
| Open Datasets | Yes | To maintain compatibility with prior work on interpretable and robust decision trees (Ustun & Rudin, 2019; Lin et al., 2020), we use the following pre-processed datasets from their repositories adult, bank, breastcancer, mammo, mushroom, spambase, careval, ficobin, and campasbin. We also use some datasets from other sources such as LIBSVM (Chang & Lin, 2011) datasets and Moro et al. (2014). |
| Dataset Splits | Yes | We use a 5-fold cross-validation based on accuracy for hyperparameters selection. For DT and Rob DT, we search through 5, 10, . . . 30 for the maximum depth of the tree. For BBM-RS, we search through 5, 10, . . . 30 for the maximum number of weak learners (T). The algorithm stops when it reaches T iterations or if no weak learner can produce a weighted accuracy > 0.51. For LCPA, we search through 5, 10, . . . 30 for the maximum 0 norm of the weight vector. We set the robust radius for Rob DT and the noise level for BBM-RS to 0.05. More details about the setup of the algorithms can be found in Appendix B. We use a 5-fold cross-validation based on accuracy for hyperparameters selection. The data is randomly split into training and testing sets by 2:1. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions using `scikit-learn` and `LIBSVM` datasets in the context of general practices and related work (e.g., Pedregosa et al., 2011; Chang & Lin, 2011), but it does not specify exact version numbers for the software dependencies or libraries used in its own experimental setup or implementation. |
| Experiment Setup | Yes | For DT and Rob DT, we search through 5, 10, . . . 30 for the maximum depth of the tree. For BBM-RS, we search through 5, 10, . . . 30 for the maximum number of weak learners (T). The algorithm stops when it reaches T iterations or if no weak learner can produce a weighted accuracy > 0.51. For LCPA, we search through 5, 10, . . . 30 for the maximum 0 norm of the weight vector. We set the robust radius for Rob DT and the noise level for BBM-RS to 0.05. |