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].
Optimal Survival Trees: A Dynamic Programming Approach
Authors: Tim Huisman, Jacobus G. M. van der Linden, Emir Demirović
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments show that our method s run time even outperforms some heuristics for realistic cases while obtaining similar out-of-sample performance with the state-of-the-art. Our experiments show that Sur Tree s out-of-sample performance on average is better than CTree and similar to OST while outperforming OST in run time for realistic cases. |
| Researcher Affiliation | Academia | Tim Huisman, Jacobus G. M. van der Linden, Emir Demirovi c Delft University of Technology EMAIL, EMAIL |
| Pseudocode | Yes | Pseudocode is provided in the appendix. |
| Open Source Code | Yes | We implemented Sur Tree in C++ with a Python interface using the STree D framework (Van der Linden, De Weerdt, and Demirovi c 2023).1 https://github.com/Alg TUDelft/pystreed In our experiment setup,2 https://github.com/Tim Huisman1703/streed sa pipeline |
| Open Datasets | Yes | The real data sets are taken from the Surv Set repository (Drysdale 2022). |
| Dataset Splits | Yes | Each method is tuned using ten-fold cross-validation. We evaluate out-of-sample performance on the real data sets using five-fold cross-validation. The synthetic data is generated according to the procedure described in (Bertsimas et al. 2022)... each with a corresponding test set of 50,000 instances. |
| Hardware Specification | Yes | All experiments were run on an Intel i76600U CPU with 4GB RAM with a time-out of 10 minutes. |
| Software Dependencies | No | The paper mentions C++ and Python interface, Julia, and R implementations for different methods, but it does not specify version numbers for any of these software dependencies. |
| Experiment Setup | Yes | For Sur Tree, we tune the depth and node budget. For CTree, we tune the confidence criterion. For OST, we tune the depth and, simultaneously, OST automatically tunes the cost-complexity parameter as part of its training. We evaluate each method with a depth limit of four on five generated data sets for each combination of n {100, 200, 500, 1000, 2000, 5000} and c {0.1, 0.5, 0.8}, each with a corresponding test set of 50,000 instances. |