In genomic prediction studies, the use of machine learning methods is growing. be separated into supervised and unsupervised learning methods.
techniques are particularly useful because they can find patterns id predict outcomes.
While several studies have examined the predictive effectiveness of specific machine learning methods, there is a lack of research comparing different sets of methods.
It is essential to understand which groups of methods perform better and to weigh their advantages and disadvantages compared to conventional methods.
Supervised learning
In genomic prediction, conventional linear mixed models have proven reliable and useful. To free telemarketing leads account for genetic variation in the population, these models incorporate fixed and random effects.
These algorithms can accurately predict genomic breeding values by taking individual relationships into account.
Because of their competitive predictive performance, computational efficiency, and simplicity, linear mixed models are widely used in plant and animal breeding. They require fewer tuning parameters than other approaches, making them suitable for genomic selection.
These methods can
For genome prediction, regular regression methods such as LASSO (Minimum Absolute Minimization and Selection Operator) and backbone regression are effective tools.
These methods enable variable selection and regularization by adding a penalty term to the standard regression model.
These methods handle high-dimensional data efficiently and increase prediction accuracy by reducing insignificant signals to zero.
Constant BRB Directory regression methods are attractive options for genomic selection in both plant and animal breeding studies because they achieve a compromise between simplicity and efficiency.