Several regularization techniques are commonly employed in cancer research:
Lasso (L1) Regularization: This technique adds a penalty equal to the absolute value of the magnitude of coefficients. It helps in feature selection by shrinking some coefficients to zero, thereby eliminating non-informative variables. Ridge (L2) Regularization: This method adds a penalty equal to the square of the magnitude of coefficients. It helps in reducing the complexity of the model by shrinking the coefficients but does not eliminate any variables. Elastic Net Regularization: This is a hybrid approach that combines L1 and L2 penalties, offering a balance between feature selection and model complexity.
