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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Born-Again Tree Ensembles
Authors: Thibaut Vidal, Maximilian Schiffer
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical studies which allow to analyze the characteristics of the born-again trees in terms of interpretability and accuracy. Further, these studies show that our algorithm is amenable to a wide range of real-world data sets. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil. 2TUM School of Management, Technical University of Munich, Munich, Germany. |
| Pseudocode | Yes | Algorithm 1 BORN-AGAIN(z L, z R) |
| Open Source Code | Yes | Detailed computational results, data, and source codes are available in the supplementary material and at the following address: https://github.com/vidalt/BA-Trees. |
| Open Datasets | Yes | We focus on a set of six datasets from the UCI machine learning repository [UCI] and from previous work by Smith et al. (1988) [Smith Et Al] and Hu et al. (2019) [Hu Et Al] |
| Dataset Splits | Yes | To obtain discrete numerical features, we used one-hot encoding on categorical data and binned continuous features into ten ordinal scales. Then, we generated training and test samples for all data sets using a ten-fold cross validation. |
| Hardware Specification | Yes | All our experiments were run on a single thread of an Intel(R) Xeon(R) CPU E5-2620v4 2.10GHz, with 128GB of available RAM, running Cent OS v7.7. |
| Software Dependencies | Yes | The DP algorithm was implemented in C++ and compiled with GCC 9.2.0 using flag -O3, whereas the original random forests were generated in Python (using scikit-learn v0.22.1). |
| Experiment Setup | Yes | Finally, for each fold and each dataset, we generated a random forest composed of ten trees with a maximum depth of three (i.e., eight leaves at most), considering p/2 random candidate features at each split. |