Learning Global Transparent Models consistent with Local Contrastive Explanations
Authors: Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now empirically validate our method. We first describe the setup, followed by a discussion of the experimental results. |
| Researcher Affiliation | Industry | Tejaswini Pedapati IBM Research tejaswinip@us.ibm.com Avinash Balakrishnan IBM Research avinash.bala@us.ibm.com Karthikeyan Shanmugan IBM Research karthikeyan.shanmugam2@ibm.com Amit Dhurandhar IBM Research adhuran@us.ibm.com |
| Pseudocode | Yes | Algorithm 1 Global Boolean Feature Learning (GBFL)., Algorithm 2 KDE based Grid Point Generation (GPG)., Algorithm 3 Model Generation using Local Explanations. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology, nor does it explicitly state that the code is available or will be released. |
| Open Datasets | Yes | We experimented on six publicly available datasets from Kaggle and UCI repository namely; Sky Survey, Credit Card, Magic, Diabetes, Waveform and WDBC. |
| Dataset Splits | Yes | Statistically significant results that measure performance based on paired t-test are reported, which are computed over 5 randomizations with 75/25% train/test split. 10-fold cross-validation was used to find all parameters including tree heights ( ≤ 5) for DT. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions software components like 'Decision trees (DTs)' and 'CART algorithm' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Decision trees (DTs) with height 5 were the transparent learner based on the CART algorithm. ... 10-fold cross-validation was used to find all parameters including tree heights ( ≤ 5) for DT. |