Evidence map›Paper›PMID 39959653›Full record

ArticleProceedings of machine learning research2023

A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data.

Ethan Harvey, Wansu Chen, David M Kent, Michael C Hughes

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Proceedings of machine learning research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ethan HarveyDepartment of Computer Science, Tufts University, Medford, MA, USA.
Wansu ChenDepartment of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
David M KentPredictive Analytics and Comparative Effectiveness Center, Tufts Medical Center, Boston, MA, USA.
Michael C HughesDepartment of Computer Science, Tufts University, Medford, MA, USA.

Funding

Enabling Comparative Effectiveness Research in Silent Brain Infarction Through Natural Language Processing and Big DataR01NS102233 · NINDS · TUFTS MEDICAL CENTER · PI DAVID M KENT · 2017 to 2026
$3.4M
Covert Cerebrovascular Disease Detected by Artificial Intelligence (C2D2AI): A Platform for Pragmatic Evidence Generation for Stroke and Dementia PreventionRF1NS102233 · NINDS · TUFTS MEDICAL CENTER · PI KENT, DAVID M · 2023 to 2025
$3.1M
Covert Cerebrovascular Disease Detected by Artificial Intelligence (C2D2AI): Pragmatic Neuroimaging Biomarkers for Future Stroke and Dementia RiskR01NS134859 · NINDS · TUFTS MEDICAL CENTER · PI DAVID M KENT · 2024 to 2026
$2.3M
NINDS NIH HHS R01 NS102233NINDS NIH HHS R01 NS134859NINDS NIH HHS RF1 NS102233
6 · The paper itself

Abstract

Practitioners building classifiers often start with a smaller pilot dataset and plan to grow to larger data in the near future. Such projects need a toolkit for extrapolating how much classifier accuracy may improve from a 2x, 10x, or 50x increase in data size. While existing work has focused on finding a single "best-fit" curve using various functional forms like power laws, we argue that modeling and assessing the

Indexed as

Gaussian processLearning curve

Identifiers

PMID39959653
PMCPMC11826957

What OpenQuestion holds

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Registered trials

None linked

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.