I was following https://www.analyticsvidhya.com/blog/2015/02/avoid-over-fitting-regularization/ to figure out the basic understanding of regularization in machine learning applications.
Under section "Regularization basics", the authors have commented that a zero of parameter lambda corresponds to over-fit while a value of infinity corresponds to "single mean estimation" (see the attached image for the excerpt ). How is it actually estimating the single mean? Any help.
Am I correct when I say that lambda will be a vector rather than a scalar with size same as the number of features taken for the problem at hand ?