I was reading a paper and came across this sentence:
"ML methods perform well by employing regularization to reduce variance and trading off regularization bias with overfitting in practice.:
I am wondering if anyone would have any insight into this sentence. I understand that regularization involves shrinkage of parameters, but I fail to see how shrinkage reduces variance and what it means to trade off the bias? Does anyone have any further insights here? Thanks.