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].
JSAT: Java Statistical Analysis Tool, a Library for Machine Learning
Authors: Edward Raff
JMLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A small benchmark on MNIST is given in Table 1, where JSAT is compared against the same algorithm in other libraries, with JSAT adjusted to match the default parameters used by others. Error rate was measured from one run on the standard training and testing split of MNIST. |
| Researcher Affiliation | Academia | Edward Raff EMAIL Department of Computer Science and Electrical Engineering University of Maryland, Baltimore County |
| Pseudocode | No | The paper describes the functionalities of the JSAT library and provides a code example in Listing 1, but it does not contain explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | JSAT is made available under the GNU GPL license here: https://github.com/Edward Raff/JSAT. |
| Open Datasets | Yes | A small benchmark on MNIST is given in Table 1, where JSAT is compared against the same algorithm in other libraries, with JSAT adjusted to match the default parameters used by others. Error rate was measured from one run on the standard training and testing split of MNIST. |
| Dataset Splits | Yes | Error rate was measured from one run on the standard training and testing split of MNIST. |
| Hardware Specification | No | The paper does not provide specific hardware details (like CPU/GPU models or memory) used for running the experiments. It only mentions 'single-threaded executions'. |
| Software Dependencies | Yes | It is written in Java 6 and has no dependencies, making it easy to integrate into any Java project without conflict. |
| Experiment Setup | No | JSAT adjusted to match the default parameters used by others. The paper describes the framework for parameter search within JSAT but does not provide specific hyperparameter values or detailed training configurations used for the benchmark results in Table 1. |