Evaluation metrics and statistical tests for machine learning

Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the...

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Vydané v:	Scientific reports Ročník 14; číslo 1; s. 6086 - 14
Hlavní autori:	Rainio, Oona, Teuho, Jarmo, Klén, Riku
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 13.03.2024 Nature Publishing Group Nature Portfolio
Predmet:	639/705/117 639/705/531 Accuracy Bronchopulmonary infection Classification Evaluation metrics Humanities and Social Sciences Image processing Image Processing, Computer-Assisted - methods Information processing Learning algorithms Lung cancer Machine Learning Mathematical models Medical images multidisciplinary Neural networks Neural Networks, Computer Performance evaluation Positron emission tomography Science Science (multidisciplinary) Statistical analysis Statistical testing Supervised Machine Learning Tomography X-rays Statistical testing Medical images Evaluation metrics Machine learning
ISSN:	2045-2322, 2045-2322
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Abstract	Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images.
AbstractList	Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images. Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images.Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images. Abstract Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other. Here, we introduce the most common evaluation metrics used for the typical supervised ML tasks including binary, multi-class, and multi-label classification, regression, image segmentation, object detection, and information retrieval. We explain how to choose a suitable statistical test for comparing models, how to obtain enough values of the metric for testing, and how to perform the test and interpret its results. We also present a few practical examples about comparing convolutional neural networks used to classify X-rays with different lung infections and detect cancer tumors in positron emission tomography images.
ArticleNumber	6086
Author	Teuho, Jarmo Klén, Riku Rainio, Oona
Author_xml	– sequence: 1 givenname: Oona orcidid: 0000-0002-7775-7656 surname: Rainio fullname: Rainio, Oona email: ormrai@utu.fi organization: Turku PET Centre, University of Turku and Turku University Hospital – sequence: 2 givenname: Jarmo orcidid: 0000-0001-9401-0725 surname: Teuho fullname: Teuho, Jarmo organization: Turku PET Centre, University of Turku and Turku University Hospital – sequence: 3 givenname: Riku orcidid: 0000-0002-0982-8360 surname: Klén fullname: Klén, Riku organization: Turku PET Centre, University of Turku and Turku University Hospital
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/38480847$$D View this record in MEDLINE/PubMed
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Snippet	Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with... Abstract Research on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with...
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SubjectTerms	639/705/117 639/705/531 Accuracy Bronchopulmonary infection Classification Evaluation metrics Humanities and Social Sciences Image processing Image Processing, Computer-Assisted - methods Information processing Learning algorithms Lung cancer Machine Learning Mathematical models Medical images multidisciplinary Neural networks Neural Networks, Computer Performance evaluation Positron emission tomography Science Science (multidisciplinary) Statistical analysis Statistical testing Supervised Machine Learning Tomography X-rays
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Title	Evaluation metrics and statistical tests for machine learning
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