About: Automated machine learning for studying the trade-off between predictive accuracy and interpretability

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint http://eprints.org/ontology/EPrint Article Article Academic Article Academic Article
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http://eprints.org/ontology/hasDocument	Automated machine learning for studying the trade-off between predictive accuracy and interpretability (PDF) Automated machine learning for studying the trade-off between predictive accuracy and interpretability (PDF) Automated machine learning for studying the trade-off between predictive accuracy and interpretability (Other) Automated machine learning for studying the trade-off between predictive accuracy and interpretability (Other) Automated machine learning for studying the trade-off between predictive accuracy and interpretability (Other) Automated machine learning for studying the trade-off between predictive accuracy and interpretability (Other) Automated machine learning for studying the trade-off between predictive accuracy and interpretability (Other) Automated machine learning for studying the trade-off between predictive accuracy and interpretability (Other) Automated machine learning for studying the trade-off between predictive accuracy and interpretability (Other) Automated machine learning for studying the trade-off between predictive accuracy and interpretability (Other) Automated machine learning for studying the trade-off between predictive accuracy and interpretability (Other) Automated machine learning for studying the trade-off between predictive accuracy and interpretability (Other)
dc:hasVersion	Automated machine learning for studying the trade-off between predictive accuracy and interpretability (PDF) Automated machine learning for studying the trade-off between predictive accuracy and interpretability (PDF)
Title	Automated machine learning for studying the trade-off between predictive accuracy and interpretability Automated machine learning for studying the trade-off between predictive accuracy and interpretability
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-08-23 2019-08-23
Creator	A. Freitas A. Freitas
status	peer reviewed peer reviewed published published
Publisher	Springer Springer
abstract	Automated Machine Learning (Auto-ML) methods search for the best classification algorithm and its best hyper-parameter settings for each input dataset. Auto-ML methods normally maximize only predictive accuracy, ignoring the classification model’s interpretability – an important criterion in many applications. Hence, we propose a novel approach, based on Auto-ML, to investigate the trade-off between the predictive accuracy and the interpretability of classification-model representations. The experiments used the Auto-WEKA tool to investigate this trade-off. We distinguish between white box (interpretable) model representations and two other types of model representations: black box (non-interpretable) and grey box (partly interpretable). We consider as white box the models based on the following 6 interpretable knowledge representations: decision trees, If-Then classification rules, decision tables, Bayesian network classifiers, nearest neighbours and logistic regression. The experiments used 16 datasets and two runtime limits per Auto-WEKA run: 5 h and 20 h. Overall, the best white box model was more accurate than the best non-white box model in 4 of the 16 datasets in the 5-hour runs, and in 7 of the 16 datasets in the 20-hour runs. However, the predictive accuracy differences between the best white box and best non-white box models were often very small. If we accept a predictive accuracy loss of 1% in order to benefit from the interpretability of a white box model representation, we would prefer the best white box model in 8 of the 16 datasets in the 5-hour runs, and in 10 of the 16 datasets in the 20-hour runs. Automated Machine Learning (Auto-ML) methods search for the best classification algorithm and its best hyper-parameter settings for each input dataset. Auto-ML methods normally maximize only predictive accuracy, ignoring the classification model’s interpretability – an important criterion in many applications. Hence, we propose a novel approach, based on Auto-ML, to investigate the trade-off between the predictive accuracy and the interpretability of classification-model representations. The experiments used the Auto-WEKA tool to investigate this trade-off. We distinguish between white box (interpretable) model representations and two other types of model representations: black box (non-interpretable) and grey box (partly interpretable). We consider as white box the models based on the following 6 interpretable knowledge representations: decision trees, If-Then classification rules, decision tables, Bayesian network classifiers, nearest neighbours and logistic regression. The experiments used 16 datasets and two runtime limits per Auto-WEKA run: 5 h and 20 h. Overall, the best white box model was more accurate than the best non-white box model in 4 of the 16 datasets in the 5-hour runs, and in 7 of the 16 datasets in the 20-hour runs. However, the predictive accuracy differences between the best white box and best non-white box models were often very small. If we accept a predictive accuracy loss of 1% in order to benefit from the interpretability of a white box model representation, we would prefer the best white box model in 8 of the 16 datasets in the 5-hour runs, and in 10 of the 16 datasets in the 20-hour runs.
Is Part Of	Financial Cryptography and Data Security Financial Cryptography and Data Security https://kar.kent.ac.uk/id/repository https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/77014#authors https://kar.kent.ac.uk/id/eprint/77014#authors
presented at	Third IFIP International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE 2019) Third IFIP International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE 2019)
volume	11713 11713
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #77014 Automated machine learning for studying the trade-off between predictive accuracy and interpretability HTML Summary of #77014 Automated machine learning for studying the trade-off between predictive accuracy and interpretability

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