A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Evaluation metrics and statistical tests for machine learning
Tekijät: Rainio, Oona; Teuho, Jarmo; Klén, Riku
Kustantaja: Nature Research
Julkaisuvuosi: 2024
Journal: Scientific Reports
Tietokannassa oleva lehden nimi: Scientific Reports
Artikkelin numero: 6086
Vuosikerta: 14
DOI: https://doi.org/10.1038/s41598-024-56706-x
Verkko-osoite: https://doi.org/10.1038/s41598-024-56706-x
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/387398800
Lisätietoja: Author correction to this article: https://www.nature.com/articles/s41598-024-66611-y ; DOI: 10.1038/s41598-024-66611-y
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
