Results 241 to 250 of about 67,619 (257)

A Review of Framework for Machine Learning Interpretability

2022
Ivo de Abreu Araújo, Renato Hidaka Torres, Nelson Cruz Sampaio Neto   +2 more
openaire   +1 more source

Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield

Agricultural and Forest Meteorology, 2023
Yanlan Liu, Kaiguang Zhao, Jay Martin
exaly  

A translucent box: interpretable machine learning in ecology

Ecological Monographs, 2020
Tim C Lucas
exaly  

Model-agnostic interpretable machine learning

2022
openaire   +2 more sources

Interpretable Machine Learning

2019
Brad Boehmke, Brandon Greenwell
openaire   +1 more source

MACHINE LEARNING MODEL INTERPRETATION

2019
GUPTA PIYUSH, PURI NIKAASH, KRISHNAMURTHY BALAJI   +2 more
openaire   +2 more sources

How Interpretable Is “Interpretable” Machine Learning?

MIT Case Studies in Social and Ethical Responsibilities of Computing
Ho Chit Siu, Kevin Leahy, Makai Mann
openaire   +1 more source

Nonlinear control of climate, hydrology, and topography on streamflow response through the use of interpretable machine learning across the contiguous United States Open Access

Journal of Water and Climate Change, 2023
exaly  

Interpretable Machine Learning

2018
openaire   +1 more source

Large language model-based interpretable machine learning control in building energy systems

Energy and Buildings
Liang Zhang, Zhelun Chen
exaly