Improving Decision Sparsity
Authors: Yiyang Sun, Tong Wang, Cynthia Rudin
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate whether our proposed methods would achieve sparser, more credible and closer explanations, we present experiments on seven datasets: (i) UCI Adult Income dataset for predicting income levels [Dua and Graff, 2017], (ii) FICO Home Equity Line of Credit Dataset for assessing credit risk, used for the Explainable Machine Learning Challenge [FICO, 2018], (iii) UCI German Credit dataset for determining creditworthiness [Dua and Graff, 2017], (iv) MIMIC-III dataset for predicting patient outcomes in intensive care units [Johnson et al., 2016a,b], (v) COMPAS dataset [Jeff Larson and Angwin, 2016, Wang et al., 2022a] for predicting recidivism, (vi) Diabetes dataset [Strack et al., 2014] for predicting whether patients will be re-admitted within two years, and (vii) Headline dataset for predicting whether the headline is likely to be shared by readers [Chen et al., 2023]. |
| Researcher Affiliation | Academia | Yiyang Sun Duke University Tong Wang Yale University Cynthia Rudin Duke University |
| Pseudocode | Yes | Algorithm 1 Reference Search for Flexible SEV (Appendix D). Algorithm 2 Preprocessing Information collection process for SEVT (Appendix E). Algorithm 3 Efficient SEVT Calculation Negative Pathways Check (Appendix E). |
| Open Source Code | Yes | Yes, we have provided the code for training, and evaluation in the Experiment folder, and the script for running in Script folder. |
| Open Datasets | Yes | UCI Adult Income dataset for predicting income levels [Dua and Graff, 2017], FICO Home Equity Line of Credit Dataset for assessing credit risk [FICO, 2018], MIMIC-III dataset for predicting patient outcomes in intensive care units [Johnson et al., 2016a,b], COMPAS dataset [Jeff Larson and Angwin, 2016, Wang et al., 2022a], Diabetes dataset [Strack et al., 2014], and Headline dataset [Chen et al., 2023]. |
| Dataset Splits | No | The datasets were divided into training and test sets using an 80-20 stratification. The paper specifies train and test splits but does not explicitly provide percentages or counts for a separate validation split. |
| Hardware Specification | Yes | All the models are trained using a RTX2080Ti GPU, and with 4 core in Intel(R) Xeon(R) Gold 6226 CPU @ 2.70GHz. |
| Software Dependencies | No | Baseline models were fit using sklearn [Pedregosa et al., 2011] implementations in Python. The resulting loss was minimized via gradient descent in Py Torch [Paszke et al., 2019]. (This mentions software packages but does not provide specific version numbers for them.) |
| Experiment Setup | Yes | The 2-layer MLP used ReLU activation and consisted of two fully-connected layers with 128 nodes each. It was trained with early stopping. The gradient-boosted classifier used 200 trees with a max depth of 3. The resulting loss was minimized via gradient descent in Py Torch [Paszke et al., 2019], with a batch size of 128, a learning rate of 0.1, and the Adam optimizer. The first 80 training epochs are warm-up epochs optimizing just Binary Cross Entropy Loss for classification (BCELoss). The next 20 epochs add the All-Opt terms. |