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
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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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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-56706-x