Tag: SHAP interpretability

A new study finds that random forest models outperform neural networks in predicting arsenic contamination across Ghana's Pra, Ankobra, and ...

A new deep learning framework for river flow forecasting puts flood peaks, uncertainty, and interpretability at the center of model ...

Researchers used seven machine-learning models, led by Transformer and LSTM architectures, to reconstruct deep soil temperatures in cold-region canal slopes ...

Researchers in Vietnam used machine learning to optimize an epoxy nanocomposite coating that is simultaneously fire-safe, mechanically strong, electrically conductive, ...

A hybrid NARX–XGBoost machine learning framework predicts daily milk yields in heat-stressed dairy cows with substantially higher accuracy than existing ...

Canadian researchers combined surface-enhanced Raman spectroscopy with deep learning to predict cysteine levels across twenty pea cultivars, achieving strong results ...

An optimized gradient boosting model predicts biochar cation exchange capacity with high accuracy from biomass composition and pyrolysis conditions, validated ...