Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute
Authors: Tong Wang
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Experiments, We perform a detailed experimental evaluation of Hy RS using public datasets and a real-world application. |
| Researcher Affiliation | Academia | 1Department of Business Analytics, University of Iowa, Iowa, USA. Correspondence to: Tong Wang <tong-wang@uiowa.edu>. |
| Pseudocode | Yes | Algorithm 1 Stochastic Local Search algorithm |
| Open Source Code | Yes | Code for Hy RS is available at https://github.com/wangtongada/Hy RS |
| Open Datasets | Yes | We use four structured datasets and a text dataset from domains where interpretability is highly desired, including healthcare, judiciaries and customer analysis. 1) juvenile(Osofsky, 1995)... 2) credit card... (Yeh & Lien, 2009) 3) recidivism... 4) readmission... 5) Yelp review (Kotzias et al., 2015)... and Lichman, M. Uci machine learning repository, 2013. URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | Yes | We partition each dataset into 80% training and 20% testing. We do cross-validation for parameter tuning on the training set and evaluate the best model on the test set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models, memory, or specific cloud instances. |
| Software Dependencies | No | The paper mentions software components like Random Forests, Ada Boost, XGBoost, and LSTM, but does not provide specific version numbers for these libraries or the underlying programming environment. |
| Experiment Setup | Yes | We train the network for 200 epochs and θ1 controls the number of rules and is chosen from [0.001, 0.01]. θ2 controls transparency and we choose θ2 from 0 to 1. |