Home ›
Entities
› academia
› Methodological Issues in Evaluating Machine Learning Models for EEG Seizure Prediction: Good Cross-Validation Accuracy Does Not Guarantee Generalization to New Patients
Methodological Issues in Evaluating Machine Learning Models for EEG Seizure Prediction: Good Cross-Validation Accuracy Does Not Guarantee Generalization to New Patients
Research article (Applied Sciences, 2023) · cited 29× · AI/ML
Methodological Issues in Evaluating Machine Learning Models for EEG Seizure Prediction: Good Cross-Validation Accuracy Does Not Guarantee Generalization to New Patients
Summary
Methodological Issues in Evaluating Machine Learning Models for EEG Seizure Prediction: Good Cross-Validation Accuracy Does Not Guarantee Generalization to New Patients is a scholarly article[1].
Key Facts
Methodological Issues in Evaluating Machine Learning Models for EEG Seizure Prediction: Good Cross-Validation Accuracy Does Not Guarantee Generalization to New Patients's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Methodological Issues in Evaluating Machine Learning Models for EEG Seizure Prediction: Good Cross-Validation Accuracy Does Not Guarantee Generalization to New Patients. Retrieved May 24, 2026, from https://4ort.xyz/entity/methodological-issues-in-evaluating-machine-learning-models-for-eeg-seizure-prediction-good-cross-validation-accuracy-do
MLA“Methodological Issues in Evaluating Machine Learning Models for EEG Seizure Prediction: Good Cross-Validation Accuracy Does Not Guarantee Generalization to New Patients.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/methodological-issues-in-evaluating-machine-learning-models-for-eeg-seizure-prediction-good-cross-validation-accuracy-do.
BibTeX@misc{4ortxyz_methodological-issues-in-evaluating-machine-learning-models-for-eeg-seizure-prediction-good-cross-validation-accuracy-do_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Methodological Issues in Evaluating Machine Learning Models for EEG Seizure Prediction: Good Cross-Validation Accuracy Does Not Guarantee Generalization to New Patients}}, year = {2026}, url = {https://4ort.xyz/entity/methodological-issues-in-evaluating-machine-learning-models-for-eeg-seizure-prediction-good-cross-validation-accuracy-do}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Methodological Issues in Evaluating Machine Learning Models for EEG Seizure Prediction: Good Cross-Validation Accuracy Does Not Guarantee Generalization to New Patients — https://4ort.xyz/entity/methodological-issues-in-evaluating-machine-learning-models-for-eeg-seizure-prediction-good-cross-validation-accuracy-do (retrieved 2026-05-24)
