An interpretable machine learning approach to understanding the impacts of attitudinal and ridesourcing factors on electric vehicle adoption

2021 ◽  
pp. 1-12
Author(s):  
Javier Bas ◽  
Zhenpeng Zou ◽  
Cinzia Cirillo
Keyword(s):  
Machine Learning ◽  
Electric Vehicle ◽  
Learning Approach ◽  
Interpretable Machine Learning ◽  
Machine Learning Approach ◽  
Electric Vehicle Adoption

scholarly journals Machine learning approach for electric vehicle availability forecast to provide vehicle-to-home services

2021 ◽  
Vol 7 ◽  
pp. 71-80
Author(s):  
Donovan Aguilar-Dominguez ◽  
Jude Ejeh ◽  
Alan D.F. Dunbar ◽  
Solomon F. Brown
Keyword(s):  
Machine Learning ◽  
Electric Vehicle ◽  
Learning Approach ◽  
Machine Learning Approach

Interpretable Machine Learning for COVID-19 Diagnosis Through Clinical Variables

2020 ◽  
Author(s):  
Lucas M. Thimoteo ◽  
Marley M. Vellasco ◽  
Jorge M. do Amaral ◽  
Karla Figueiredo ◽  
Cátia Lie Yokoyama ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Model ◽  
Linear Models ◽  
Learning Approach ◽  
Clinical Variables ◽  
Interpretable Machine Learning ◽  
Machine Learning Approach ◽  
Feature Importance ◽  
Non Linear ◽  
The Difference

This work proposes an interpretable machine learning approach to diagnosesuspected COVID-19 cases based on clinical variables. Results obtained for the proposed models have F-2 measure superior to 0.80 and accuracy superior to 0.85. Interpretation of the linear model feature importance brought insights about the most relevant features. Shapley Additive Explanations were used in the non-linear models. They were able to show the difference between positive and negative patients as well as offer a global interpretability sense of the models.

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