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..
Learning From Small Samples: An Analysis of Simple Decision Heuristics
Authors: Özgür Şimşek, Marcus Buckmann
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical analysis is the most extensive to date, employing 63 natural data sets on diverse subjects. |
| Researcher Affiliation | Academia | Ozg ur S ims ek and Marcus Buckmann Center for Adaptive Behavior and Cognition Max Planck Institute for Human Development Lentzeallee 94, 14195 Berlin, Germany EMAIL |
| Pseudocode | No | The paper describes algorithms verbally and mathematically but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | The data sets were gathered from a wide variety of sources, including online data repositories, textbooks, packages for R statistical software, statistics and data mining competitions, research publications, and individual scientists collecting field data. The data sets are described in detail in the supplementary material. |
| Dataset Splits | Yes | Figure 4 shows accuracies when the models were trained on 50% of the objects and tested on the remaining 50%... We used the CART implementation in rpart [15]... and 10-fold cross-validated cost-complexity pruning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | Yes | We used the CART implementation in rpart [15]... [15] T. Therneau, B. Atkinson, and B. Ripley. rpart: Recursive partitioning and regression trees, 2014. R package version 4.1-5. |
| Experiment Setup | Yes | We used the CART implementation in rpart [15] with the default splitting criterion Gini, cp=0, minsplit=2, minbucket=1, and 10-fold cross-validated cost-complexity pruning. |