Tag: SHAP

A hybrid machine learning framework combining seven feature selection methods with XGBoost achieves near-perfect daily reference evapotranspiration estimates from just ...

Researchers have developed ZD-HybridNet, a hybrid Transformer-LSTM deep learning framework that detects attack categories withheld from training and explains its ...

A new explainable machine learning framework combining EEG signals with clinical data distinguishes Alzheimer's disease from frontotemporal dementia with 89.8 ...

A closure-aware machine-learning framework can bidirectionally predict biomass fuel properties from incomplete characterization data, outperforming classic empirical correlations while flagging ...

Researchers in Nanjing developed an interpretable machine learning model that analyzes MRI tumor subregions to predict prostate cancer aggressiveness noninvasively ...

A new framework called ARCHEX uses archetypal analysis to reveal global structural patterns in machine learning models that local explanation ...

A new deep learning model that embeds Turing-inspired reaction-diffusion mathematics and attention mechanisms into a ResNet50 backbone classifies dermoscopic images ...

Finnish researchers have built an explainable machine learning framework that segments B2B customers, predicts purchases with F1-scores above 0.92, and ...

A new CNN–LSTM–attention framework combines SHAP explainability and Marabou formal verification to deliver reliable, interpretable injury risk prediction from physiological ...

Machine learning analysis of oral rinse samples from The Maastricht Study identified the protein REG1A as a non-invasive marker of ...