Statistics and Estimation
Fitting a model is estimating parameters from a finite sample — and every estimate comes with uncertainty about how wrong it might be. This page derives maximum likelihood estimation, the principle underlying nearly every loss function used in this knowledge base, and shows the bridge from "most likely parameters" to "squared error" and "cross-entropy."