There’s one wide-spread statement in statistics and machine learning literature that increased flexibility comes with decreased robustness in the test set. However, is it always true?

Why’s TSS (total sum of squares) equal to ESS (explained sum of squares) plus RSS (residual sum of squares)?

Markov’s Inequality, Chebyshev’s Inequality, and Cauchy—Schwarz Inequality