.png){kind=icon}
.png){kind=icon}
Return
REFORMS: Consensus-based Recommendations for Machine-learning-based Science
.png){kind=icon}
OA
AI
DOI：10.1126/sciadv.adk3452.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear recommendations for conducting and reporting ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist (recommendations for machine-learning-based science). It consists of 32 questions and a paired set of guidelines. REFORMS was developed on the basis of a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility. We provide a checklist to improve reporting practices in ML-based science based on a review of best practices and common errors.
Keywords:
CROSS-VALIDATION
MISSING DATA
MODEL SELECTION
PREDICTION
QUALITY
HEALTH
REPRODUCIBILITY
PERFORMANCE
PSYCHOLOGY
TRIALS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
12.5
Papers:
2.0W
Citations:
18.1W
View more organizations