ReviewPatterns (New York, N.Y.)2020
Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines.
Review in Patterns (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Neyman-Pearson Multi-class Classification via Cost-sensitive Learning.Journal of the American Statistical Association · 2025Article
- The risk of shortcutting in deep learning algorithms for medical imaging research.Scientific reports · 2024Article
- Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2024Article
- Automation in canine science: enhancing human capabilities and overcoming adoption barriers.Frontiers in veterinary science · 2024Article
- Article
- Machine learning models for predicting depression in Korean young employees.Frontiers in public health · 2023Article
- Anlotinib Combined with Toripalimab as Second-Line Therapy for Advanced, Relapsed Gastric or Gastroesophageal Junction Carcinoma.The oncologist · 2022Article
- Theory and Practice of Integrating Machine Learning and Conventional Statistics in Medical Data Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- Automatic identification of tinnitus malingering based on overt and covert behavioral responses during psychoacoustic testing.NPJ digital medicine · 2022Article
- Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA Profiles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- The use patterns of novel psychedelics: experiential fingerprints of substituted phenethylamines, tryptamines and lysergamides.Psychopharmacology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Making binary decisions is a common data analytical task in scientific research and industrial applications. In data sciences, there are two related but distinct strategies: hypothesis testing and binary classification. In practice, how to choose between these two strategies can be unclear and rather confusing. Here, we summarize key distinctions between these two strategies in three aspects and list five practical guidelines for data analysts to choose the appropriate strategy for specific analysis needs. We demonstrate the use of those guidelines in a cancer driver gene prediction example.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.