regularization

"Use regularization" is the answer everyone gives. The ordered toolbox, and which tool to reach for first when a customer's pilot model collapses in week two, is what actually gets scored.

MAP is MLE plus a prior, and that prior is not academic decoration: it is the same thing as regularization, and it is what stops a model from making confident nonsense out of three data points. The connection that makes this an applied answer, not a stats-class recitation.

More features is not more signal, it is more variance, more leakage surface, and a thinner data manifold. The disciplined answer ranks filter, embedded, and wrapper methods by cost, leans on L1, and screens every survivor for leakage and stability.