<italic>SAMA</italic>: Spatially-Aware Model-Agnostic Machine Learning Framework for Geophysical Data

<italic>SAMA</italic>: Spatially-Aware Model-Agnostic Machine Learning Framework for Geophysical Data

Geophysical data is a form of spatial data that suffers from various limitations when applying conventional machine learning algorithms and evaluation techniques. A key limitation facing models trained on geophysical data is their inability to generalize well when deployed to predict from new unseen...

Full description

Bibliographic Details
Main Authors: Asma Z. Yamani, Klemens Katterbaeur, Abdallah A. Alshehri, Rabeah A. Al-Zaidy
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Bias-variance trade-off
cross-fold validation
feature engineering
feature selection
geophysical data
random forest
Online Access:https://ieeexplore.ieee.org/document/10016733/

Internet

https://ieeexplore.ieee.org/document/10016733/

Similar Items