bias variance

The most common ML fundamentals question, and a quiet seniority test: anyone recites the definition, but can you decompose the error and turn it into a concrete debugging plan?

Ensembles dominate tabular ML, and this question checks whether you know they attack different parts of the error. The signal is bagging-reduces-variance vs boosting-reduces-bias and why GBMs win on tabular data. Here is the answer.

Classic bias-variance says bigger models eventually overfit, yet deep nets keep improving past the point where they memorize the data. The signal is explaining the second descent and why over-parameterized models generalize. Here is the answer.