Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability

10.1016/j.patter.2020.100129

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Bibliographic Details
Main Authors:	Ho, S.Y., Phua, K., Wong, L., Bin Goh, W.W.
Other Authors:	DEPARTMENT OF COMPUTER SCIENCE
Format:	Review
Published:	Cell Press 2021
Subjects:	computational biology data science descriptive statistics DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems exploratory data analysis scientific method
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/199384
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Institution:	National University of Singapore

id	sg-nus-scholar.10635-199384
record_format	dspace
spelling	sg-nus-scholar.10635-1993842024-04-25T07:33:28Z Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability Ho, S.Y. Phua, K. Wong, L. Bin Goh, W.W. DEPARTMENT OF COMPUTER SCIENCE computational biology data science descriptive statistics DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems exploratory data analysis scientific method 10.1016/j.patter.2020.100129 Patterns 1 8 100129 2021-08-25T14:16:27Z 2021-08-25T14:16:27Z 2020 Review Ho, S.Y., Phua, K., Wong, L., Bin Goh, W.W. (2020). Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability. Patterns 1 (8) : 100129. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patter.2020.100129 26663899 https://scholarbank.nus.edu.sg/handle/10635/199384 Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Cell Press Scopus OA2020
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	computational biology data science descriptive statistics DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems exploratory data analysis scientific method
spellingShingle	computational biology data science descriptive statistics DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems exploratory data analysis scientific method Ho, S.Y. Phua, K. Wong, L. Bin Goh, W.W. Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability
description	10.1016/j.patter.2020.100129
author2	DEPARTMENT OF COMPUTER SCIENCE
author_facet	DEPARTMENT OF COMPUTER SCIENCE Ho, S.Y. Phua, K. Wong, L. Bin Goh, W.W.
format	Review
author	Ho, S.Y. Phua, K. Wong, L. Bin Goh, W.W.
author_sort	Ho, S.Y.
title	Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability
title_short	Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability
title_full	Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability
title_fullStr	Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability
title_full_unstemmed	Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability
title_sort	extensions of the external validation for checking learned model interpretability and generalizability
publisher	Cell Press
publishDate	2021
url	https://scholarbank.nus.edu.sg/handle/10635/199384
_version_	1800914987049287680

Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability

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