Constrained Prescriptive Trees via Column Generation
Authors: Shivaram Subramanian, Wei Sun, Youssef Drissi, Markus Ettl4602-4610
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of our method with extensive experiments on both synthetic and real datasets. |
| Researcher Affiliation | Industry | IBM Research, Yorktown Heights, NY 10591, USA subshiva, sunw, youssefd, mettl@us.ibm.com |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly presented. |
| Open Source Code | No | The paper uses third-party open-source tools (e.g., light GBM) but does not provide a link or explicit statement about the open-sourcing of its own methodology's code. |
| Open Datasets | Yes | using a publicly available dataset2 that contains over two years of household level transactions from a group of 2,500 households at a grocery retailer. A processed dataset with 97,295 rows which contain both purchases and no-purchases of strawberries is available online.3 |
| Dataset Splits | No | The paper mentions dividing data in halves for some experiments and varying training data size, but does not provide specific or consistent train/validation/test split proportions or methodologies for all experiments. |
| Hardware Specification | Yes | All experiments were run on an Intel 8-core i7 PC with 32GB RAM. |
| Software Dependencies | Yes | CPLEX 20.1 was used to solve the RMP. |
| Experiment Setup | Yes | The minimum number of samples per rule for SPMT is set to 10. K = 100 for KSP. |