Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Prediction Rule Reshaping
Authors: Matt Bonakdarpour, Sabyasachi Chatterjee, Rina Foygel Barber, John Lafferty
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | experiments are performed to demonstrate their performance on four datasets. We find that reshaping methods enforce shape constraints without compromising predictive accuracy. We apply our methods to four datasets in Section (3) and show that they enforce the pre-specified shape constraints without sacrificing accuracy. |
| Researcher Affiliation | Academia | 1Department of Statistics, The University of Chicago 2Department of Statistics, University of Illinois at Urbana Champaign 3Department of Statistics and Data Science, Yale University. |
| Pseudocode | Yes | Algorithm 1 IISO Algorithm |
| Open Source Code | No | The paper describes the implementation details and tools used (e.g., "The BB method was implemented in R, and the OC and EX methods were implemented in R and C++, extending the R package ranger..."), but it does not provide any link or explicit statement that the code for their proposed reshaping methods is open-source or available. |
| Open Datasets | Yes | The diabetes dataset (Efron et al., 2004) consists of ten physiological baseline variables... Adult dataset (Lichman, 2013). The Spambase dataset (Lichman, 2013) |
| Dataset Splits | Yes | We apply 5-fold cross validation on all four tasks |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory, or cloud instances). |
| Software Dependencies | Yes | The BB method was implemented in R, and the OC and EX methods were implemented in R and C++, extending the R package ranger (Wright & Ziegler, 2017). The exact estimator from Section (2.2.1) is computed using the MOSEK C++ package (Ap S, 2017). |
| Experiment Setup | Yes | We apply 5-fold cross validation on all four tasks and present the results under the relevant performance metrics in Table (1). The random forest was fit with the default settings found in ranger. |