Data leakage detection in machine learning code: transfer learning, active learning, or low-shot prompting?

• Published in: PeerJ. Computer science Vol. 11; p. e2730
• Main Authors: Alturayeif, Nouf, Hassine, Jameleddine
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 05.03.2025; PeerJ Inc
• Subjects: Active learning; Artificial Intelligence; Code quality; Computational linguistics; Data leakage; Data Mining and Machine Learning; Evaluation; Language processing; Low-shot prompting; Machine learning; Natural language interfaces; Neural Networks; Software Engineering; Transfer learning; Data leakage; Code quality; Low-shot prompting; Active learning; Transfer learning
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.2730

With the increasing reliance on machine learning (ML) across diverse disciplines, ML code has been subject to a number of issues that impact its quality, such as lack of documentation, algorithmic biases, overfitting, lack of reproducibility, inadequate data preprocessing, and potential for data lea...