Results 191 to 200 of about 159,034 (226)

Machine learning for microbiologists

Nature Reviews Microbiology, 2023
Francesco Asnicar, Andrew Maltez Thomas, Levi Waldron   +2 more
exaly  

Interpretable Machine Learning Assessment

SSRN Electronic Journal, 2022
openaire   +1 more source

Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation

Chemical Engineering Journal, 2022
Teng Zhou, Kai Sundmacher, Zihao Wang
exaly  

Advancing Computational Toxicology by Interpretable Machine Learning

Environmental Science & Technology, 2023
Xuelian Jia, Tong Wang, Hao Zhu
exaly  

Interpretability of machine learning models

2019
As machine learning algorithms have been used in many areas of our lives, for example, self-driving cars, healthcare, and the financial industry, the problem of trust is becoming even more urgent. To trust the decisions that the algorithms adopt, we need to understand the nature of their occurrence, so often we need not only a theoretical understanding
Neustroev, D. D., Kurmanova, D. I.
openaire   +1 more source

Interpretable machine learning for knowledge generation in heterogeneous catalysis

Nature Catalysis, 2022
Jacques A Esterhuizen, Bryan R Goldsmith, Suljo Linic   +2 more
exaly  

An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation

Journal of Environmental Management, 2023
Ali El Bilali, Ayoub Nafii, Houda LAMANE
exaly  

Machine Learning Interpretation Tools

2020
openaire   +1 more source

Opening the Black Box: Interpretable Machine Learning for Geneticists

Trends in Genetics, 2020
Christina B Azodi, Jiliang Tang, Shin-Han Shiu   +2 more
exaly  

Towards expert–machine collaborations for technology valuation: An interpretable machine learning approach

Technological Forecasting and Social Change, 2022
Juram Kim, changyong Lee
exaly