Use of supervised machine learning for prediction of genetic values based on two generations of ancestors (Research, Society and Development, 2023)
Efficient use of tools aimed at accelerating genetic improvement such as targeted mating requires genetic value information from both breeders and mothers, however, low accuracy or lack thereof can compromise genetic improvement programs, thus estimating genetic values of females is challenging. Methodologies that use only information from male ancestors to calculate genetic values of females have already been proposed, however, the emergence of supervised machine learning technologies have made relevant contributions to genetic improvement. Therefore, this study aims to evaluate a methodology based on a supervised linear regression algorithm in function of the consolidated method in literature, which can be employed in genetic improvement programs. The results of Pearson's correlation analysis were all significant (p