官方服务:
资源简介:
Study database validating SHAP Ia modes for classifying obesity
应用场景:
创建时间:
2025-07-21
相关数据集
A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions
This dataset was produced by simulations done in "A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions"
DataCite Commons2024-07-02 更新60
Association of cord blood methylation with neonatal leptin: an epigenome wide association study. Association of cord blood methylation with neonatal leptin: an epigenome wide association study
This is a study of 114 newborns aimed at identifying associations of cord blood methylation profiles with measures of newborn adiposity. Neonatal adiposity is a risk factor for childhood obesity. Inve
NIAID Data Ecosystem40
Explainable Machine Learning for Earthquakes - dataset
This dataset supports the research presented in the paper "Explainable Machine Learning for Earthquakes: SHAP Interpretation of CNNs to Distinguish Seismic Spectrograms of Foreshocks and Aftershocks".
Zenodo2026-01-02 更新10
Additional file 10 of Development and internal validation of an interpretable machine learning model to predict coagulopathy following extracorporeal membrane oxygenation: a retrospective multicenter study
Supplementary Material 10.
NIAID Data Ecosystem50
Risk factors and their Shap values (Early warning system for At-Risk students using SHAP and OULAD with Week-2 time frame)
These are the risk factors and their SHAP values used in Early warning using OULAD and SHAP.
IEEE20



