Why is Lasso Regularization Important in Cancer Research?
In the context of cancer research, identifying relevant biomarkers from high-dimensional datasets is crucial. These datasets often contain thousands of potential predictors, such as gene expression levels, but only a small subset may be truly informative. Lasso regularization is particularly useful for biomarker discovery because it can shrink the coefficients of less important features to zero, thus automatically selecting a simpler, more interpretable model.
