When λ = 0 {\displaystyle \lambda =0} , this gives empirical risk minimization with low bias and high variance. When λ {\displaystyle \lambda } is large, the learning algorithm will have high bias and low variance. The value of λ {\displaystyle \lambda } can be chosen empirically via cross validation.