Technical nuances of machine learning: implementation and validation of supervised methods for genomic prediction in plant breeding

The decision-making process in plant breeding is driven by data. machine learning framework has powerful tools that can extract useful information from However, there still a lack of understanding about the underlying algorithms these methods, their strengths, and pitfalls. Machine two main branches: supervised unsupervised learning. In breeding, used for genomic prediction, where phenotypic traits are modeled as function molecular markers. key prediction linear kernel neural networks, tree ensembles. This manuscript provides an insight into implementation how cross-validations be to compare methods. Examples come breeding.