Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations
Authors: Tong Wang
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and realworld data demonstrate that MRS models have significantly smaller complexity and fewer features than baseline models while being competitive in predictive accuracy. |
| Researcher Affiliation | Academia | Tong Wang Tippie School of Business University of Iowa Iowa City, IA 52242 tong-wang@uiowa.edu |
| Pseudocode | No | The paper states: "See the supplementary material for the complete algorithm." This indicates the algorithm details, potentially including pseudocode, are not provided within the main body of the paper itself. |
| Open Source Code | Yes | Code: The MRS code is available at https://github.com/wangtongada/MRS. |
| Open Datasets | Yes | We then evaluate the performance of MRS on six real-world data sets from law enforcement, healthcare, and demography where interpretability is most desired. The data sets are publicly available at UCI Machine Learning Repository or ICPSR. Table 1: A summary of data sets [...] Juvenile Delinquency [23] [...] Credit card [34] [...] Census [14] [...] Hospital Readmission [27] |
| Dataset Splits | Yes | Each data set is partitioned into 75% training and 25% testing. We performed 5-fold cross validation for each method. In each fold, we set aside 20% of data during training for parameter tuning and used a grid search to locate the best set of parameters. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions using "R and python packages for the random forest, SBRL, CBA and Ripper [11]" and "the publicly available code for BRS 1", but it does not specify any version numbers for these software dependencies (e.g., Python 3.x, PyTorch 1.x, specific package versions). |
| Experiment Setup | Yes | We set entries in θ to 1, α+ = α = 100 and β+ = β = 1. αM, βM control the number of rules and αL, βL control lengths of rules. We set αM, αL to 1 and vary βM, βL. We report in Table 2 the average test error, the average number of conditions in the output model, and the average number of unique features used in each model, computed from the 5 folds. |