Learning From Small Samples: An Analysis of Simple Decision Heuristics

Authors: Özgür Şimşek, Marcus Buckmann

NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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 {ozgur, buckmann}@mpib-berlin.mpg.de
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.