ArticleFrontiers in plant science2024
Using the Pearson's correlation coefficient as the sole metric to measure the accuracy of quantitative trait prediction: is it sufficient?
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
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- Exploring spiritual health among South Korean older adults using the ICECAP-O measure: a cross-sectional study.Health and quality of life outcomes · 2026Article
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- Precise diagnosis of Alzheimer's disease based on sex-specific gray matter characteristics.Frontiers in neuroscience · 2026Article
- Normalized cumulative gain as an alternative evaluation measure for genomic selection models.Genetics, selection, evolution : GSE · 2025Article
- Lightweight Convolutional Neural Network with Efficient Channel Attention Mechanism for Real-Time Facial Emotion Recognition in Embedded Systems.Sensors (Basel, Switzerland) · 2025Article
- Modeling the Future Distribution ofBiology · 2025Article
- Clinical validation of an abridged AIMS: Streamlining motor screening in the first-year infant.Early human development · 2025Article
- Psychopathological Influences on Surgical and Clinical Outcomes in Lumbar Disk Herniation: Prediction Models and Literature Analysis.Journal of personalized medicine · 2025Article
- Comprehensive evaluation of frost mulberry leaves of 21 mulberry varieties.Frontiers in plant science · 2025Article
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7 authors.
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Abstract
How to evaluate the accuracy of quantitative trait prediction is crucial to choose the best model among several possible choices in plant breeding. Pearson's correlation coefficient (PCC), serving as a metric for quantifying the strength of the linear association between two variables, is widely used to evaluate the accuracy of the quantitative trait prediction models, and generally performs well in most circumstances. However, PCC may not always offer a comprehensive view of predictive accuracy, especially in cases involving nonlinear relationships or complex dependencies in machine learning-based methods. It has been found that many papers on quantitative trait prediction solely use PCC as a single metric to evaluate the accuracy of their models, which is insufficient and limited from a formal perspective. This study addresses this crucial issue by presenting a typical example and conducting a comparative analysis of PCC and nine other evaluation metrics using four traditional methods and four machine learning-based methods, thereby contributing to the improvement of practical applicability and reliability of plant quantitative trait prediction models. It is recommended to employ PCC in conjunction with other evaluation metrics in a targeted manner based on specific application scenarios to reduce the likelihood of drawing misleading conclusions.
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