DREAMER: a computational framework to evaluate readiness of datasets for machine learning.

ABSTRACT:

Background

Machine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains. The assessment of dataset quality stands as a pivotal precursor to the successful deployment of ML models. In this study, we introduce DREAMER (Data REAdiness for MachinE learning Research), an algorithmic framework leveraging supervised and unsupervised machine learning techniques to autonomously evaluate the suitability of tabular datasets for ML model development. DREAMER is openly accessible as a tool on GitHub and Docker, facilitating its adoption and further refinement within the research community..

Results

The proposed model in this study was applied to three distinct tabular datasets, resulting in notable enhancements in t

SUBMITTER: Ahangaran M 

PROVIDER: S-EPMC11149315 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature