Keywords
Ridge, Lasso, Elastic Net, Mean Square Error(MSE)
Abstract
In this project, we investigate several variable selection procedures to give an overview of how well they perform on a genomic dataset using three different penalized regression approaches. Comparisons between different methods were performed. These methods include Ridge, lasso, and Elastic Net. We utilized 4494 observations with 7389 SNPs gene scores to predict time to male flowering (dtoa). We assessed the performance of these three models in terms of mean square error. Not surprisingly, Lasso and Elastic Net perform better than Ridge Regression. Overall, Elastic Net performed better in predicting the time of male flowering (dtoa).
Course Name
STA 6366 Data Science 1
Instructor Name
Dr Rui Xie
STARS Citation
Yeboah, Felix, "Evaluation of Variable Selection Techniques on the Genetic Architecture of Flowering Time in Maize" (2025). Data Science and Data Mining. 33.
https://stars.library.ucf.edu/data-science-mining/33
Included in
Biology Commons, Biosecurity Commons, Data Science Commons, Genetics and Genomics Commons
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