ReviewPatterns (New York, N.Y.)2020
Avoid Oversimplifications in Machine Learning: Going beyond the Class-Prediction Accuracy.
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 12 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
12 citing papers in PubMed.
- Ten quick tips for ensuring machine learning model validity.PLoS computational biology · 2024Article
- Systems Biology and Omics Approaches for Complex Human Diseases.Biomolecules · 2023Article
- Role of brain 2-[European journal of nuclear medicine and molecular imaging · 2023Article
- Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia.Frontiers in neuroscience · 2023Article
- Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- Doppelgänger spotting in biomedical gene expression data.iScience · 2022Article
- Detecting Unusual Intravenous Infusion Alerting Patterns with Machine Learning Algorithms.Biomedical instrumentation & technology · 2022Article
- Awareness: An empirical model.Frontiers in psychology · 2022Article
- Editorial: Prediction and explanation in biomedicine using network-based approaches.Frontiers in genetics · 2022Article
- Systems serology detects functionally distinct coronavirus antibody features in children and elderly.Nature communications · 2021Article
- Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability.Patterns (New York, N.Y.) · 2020Review
- AI Naturalists Might Hold the Key to Unlocking Biodiversity Data in Social Media Imagery.Patterns (New York, N.Y.) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Class-prediction accuracy provides a quick but superficial way of determining classifier performance. It does not inform on the reproducibility of the findings or whether the selected or constructed features used are meaningful and specific. Furthermore, the class-prediction accuracy oversummarizes and does not inform on how training and learning have been accomplished: two classifiers providing the same performance in one validation can disagree on many future validations. It does not provide explainability in its decision-making process and is not objective, as its value is also affected by class proportions in the validation set. Despite these issues, this does not mean we should omit the class-prediction accuracy. Instead, it needs to be enriched with accompanying evidence and tests that supplement and contextualize the reported accuracy. This additional evidence serves as augmentations and can help us perform machine learning better while avoiding naive reliance on oversimplified metrics.
Indexed as
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.