Ridge regularization, also known as L2 regularization, is a technique used in machine learning to prevent overfitting by adding a penalty to the magnitude of the model's coefficients. The penalty term is the sum of the squares of the coefficients, scaled by a parameter lambda (λ). This technique helps in making the model more robust and generalizable to new data, which is crucial in the context of cancer research.
