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].
Encog: Library of Interchangeable Machine Learning Models for Java and C#
Authors: Jeff Heaton
JMLR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper introduces the Encog library for Java and C#, a scalable, adaptable, multiplatform machine learning framework that was first released in 2008. Encog allows a variety of machine learning models to be applied to data sets using regression, classification, and clustering. Various supported machine learning models can be used interchangeably with minimal recoding. Encog uses efficient multithreaded code to reduce training time by exploiting modern multicore processors. The current version of Encog can be downloaded from http://www.encog.org. |
| Researcher Affiliation | Academia | JeffHeaton EMAIL College of Engineering and Computing Nova Southeastern University Fort Lauderdale, FL 33314, USA |
| Pseudocode | No | The paper includes Java code snippets to demonstrate API usage but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The current version of Encog can be downloaded from http://www.encog.org. ... A complete list of contributors to Encog can be found at the Git Hub repository: https://github.com/encog. |
| Open Datasets | Yes | A classification example will demonstrate this interchangeability, using the iris data set (Fisher, 1936). |
| Dataset Splits | Yes | For model validation, 30% of the data are held back. Though the validation sampling is random, a seed of 1001 is used so that the items selected for validation remain constant between program runs. Finally, the default training type is selected. ... The example trains using a 5-fold cross-validated technique that chooses the model with the best validation score. |
| Hardware Specification | No | The paper mentions "efficient multithreaded code to reduce training time by exploiting modern multicore processors," but does not specify any particular CPU or GPU models or other detailed hardware specifications for the experiments. |
| Software Dependencies | No | The paper describes the Encog library for Java and C# but does not provide specific version numbers for Java, C#, or any other ancillary software dependencies. |
| Experiment Setup | No | The paper mentions using a "default training type" and a "5-fold cross-validated technique" for the example, but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed system-level training settings. |