Formal definition of the MARS method for quantifying the unique target class discoveries of selected machine classifiers [version 2; peer review: 2 approved]

Conventional binary classification performance metrics evaluate either general measures (accuracy, F score) or specific aspects (precision, recall) of a model’s classifying ability. As such, these metrics, derived from the model’s confusion matrix, provide crucial insight regarding classifier-data i...
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Autor*in:	Namrata Mali [verfasserIn] Felipe Restrepo [verfasserIn] Peter Ractham [verfasserIn] Alan Abrahams [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Machine learning Binary classification Classifier performance evaluation Classifier selection optimization Classifier comparative uniqueness eng

Übergeordnetes Werk:	In: F1000Research - F1000 Research Ltd, 2013, 11(2022)
Übergeordnetes Werk:	volume:11 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.12688/f1000research.110567.2

Katalog-ID:	DOAJ02843448X

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author	Namrata Mali
spellingShingle	Namrata Mali misc Machine learning misc Binary classification misc Classifier performance evaluation misc Classifier selection optimization misc Classifier comparative uniqueness misc eng misc Medicine misc R misc Science misc Q Formal definition of the MARS method for quantifying the unique target class discoveries of selected machine classifiers [version 2; peer review: 2 approved]
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topic_title	Formal definition of the MARS method for quantifying the unique target class discoveries of selected machine classifiers [version 2; peer review: 2 approved] Machine learning Binary classification Classifier performance evaluation Classifier selection optimization Classifier comparative uniqueness eng
topic	misc Machine learning misc Binary classification misc Classifier performance evaluation misc Classifier selection optimization misc Classifier comparative uniqueness misc eng misc Medicine misc R misc Science misc Q
topic_unstemmed	misc Machine learning misc Binary classification misc Classifier performance evaluation misc Classifier selection optimization misc Classifier comparative uniqueness misc eng misc Medicine misc R misc Science misc Q
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title	Formal definition of the MARS method for quantifying the unique target class discoveries of selected machine classifiers [version 2; peer review: 2 approved]
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title_full	Formal definition of the MARS method for quantifying the unique target class discoveries of selected machine classifiers [version 2; peer review: 2 approved]
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title_sort	formal definition of the mars method for quantifying the unique target class discoveries of selected machine classifiers [version 2; peer review: 2 approved]
title_auth	Formal definition of the MARS method for quantifying the unique target class discoveries of selected machine classifiers [version 2; peer review: 2 approved]
abstract	Conventional binary classification performance metrics evaluate either general measures (accuracy, F score) or specific aspects (precision, recall) of a model’s classifying ability. As such, these metrics, derived from the model’s confusion matrix, provide crucial insight regarding classifier-data interactions. However, modern- day computational capabilities have allowed for the creation of increasingly complex models that share nearly identical classification performance. While traditional performance metrics remain as essential indicators of a classifier’s individual capabilities, their ability to differentiate between models is limited. In this paper, we present the methodology for MARS (Method for Assessing Relative Sensitivity/ Specificity) ShineThrough and MARS Occlusion scores, two novel binary classification performance metrics, designed to quantify the distinctiveness of a classifier’s predictive successes and failures, relative to alternative classifiers. Being able to quantitatively express classifier uniqueness adds a novel classifier-classifier layer to the process of model evaluation and could improve ensemble model-selection decision making. By calculating both conventional performance measures, and proposed MARS metrics for a simple classifier prediction dataset, we demonstrate that the proposed metrics’ informational strengths synergize well with those of traditional metrics, delivering insight complementary to that of conventional metrics.
abstractGer	Conventional binary classification performance metrics evaluate either general measures (accuracy, F score) or specific aspects (precision, recall) of a model’s classifying ability. As such, these metrics, derived from the model’s confusion matrix, provide crucial insight regarding classifier-data interactions. However, modern- day computational capabilities have allowed for the creation of increasingly complex models that share nearly identical classification performance. While traditional performance metrics remain as essential indicators of a classifier’s individual capabilities, their ability to differentiate between models is limited. In this paper, we present the methodology for MARS (Method for Assessing Relative Sensitivity/ Specificity) ShineThrough and MARS Occlusion scores, two novel binary classification performance metrics, designed to quantify the distinctiveness of a classifier’s predictive successes and failures, relative to alternative classifiers. Being able to quantitatively express classifier uniqueness adds a novel classifier-classifier layer to the process of model evaluation and could improve ensemble model-selection decision making. By calculating both conventional performance measures, and proposed MARS metrics for a simple classifier prediction dataset, we demonstrate that the proposed metrics’ informational strengths synergize well with those of traditional metrics, delivering insight complementary to that of conventional metrics.
abstract_unstemmed	Conventional binary classification performance metrics evaluate either general measures (accuracy, F score) or specific aspects (precision, recall) of a model’s classifying ability. As such, these metrics, derived from the model’s confusion matrix, provide crucial insight regarding classifier-data interactions. However, modern- day computational capabilities have allowed for the creation of increasingly complex models that share nearly identical classification performance. While traditional performance metrics remain as essential indicators of a classifier’s individual capabilities, their ability to differentiate between models is limited. In this paper, we present the methodology for MARS (Method for Assessing Relative Sensitivity/ Specificity) ShineThrough and MARS Occlusion scores, two novel binary classification performance metrics, designed to quantify the distinctiveness of a classifier’s predictive successes and failures, relative to alternative classifiers. Being able to quantitatively express classifier uniqueness adds a novel classifier-classifier layer to the process of model evaluation and could improve ensemble model-selection decision making. By calculating both conventional performance measures, and proposed MARS metrics for a simple classifier prediction dataset, we demonstrate that the proposed metrics’ informational strengths synergize well with those of traditional metrics, delivering insight complementary to that of conventional metrics.
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title_short	Formal definition of the MARS method for quantifying the unique target class discoveries of selected machine classifiers [version 2; peer review: 2 approved]
url	https://doi.org/10.12688/f1000research.110567.2 https://doaj.org/article/c0cff347f8454c0284f3db97de4eeceb https://f1000research.com/articles/11-391/v2 https://doaj.org/toc/2046-1402
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Formal definition of the MARS method for quantifying the unique target class discoveries of selected machine classifiers [version 2; peer review: 2 approved]

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