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Noisy data; Classification; K-nearest neighbours (KNN); Random forest (RF); Support vector machines (SVM); Ridge logistic regression; Sugar beet; Binomial phenotype; Robustness to errors; Genomic predictions
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Plant Methods, doi:10.1186/s13007-016-0136-4
Noisy data; Classification; K-nearest neighbours (KNN); Random forest (RF); Support vector machines (SVM); Ridge logistic regression; Sugar beet; Binomial phenotype; Robustness to errors; Genomic predictions
“Noisy beets”: impact of phenotyping errors on genomic predictions for binary traits in Beta vulgaris
Filippo Biscarini
Nelson Nazzicari
Chiara Broccanello
Piergiorgio Stevanato
Simone Marini
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