CMP.7:8 - Common Anti-Patterns and How to Avoid Them

Misstep exposed by the method	Consequence and repair
Use zero training error as a prediction argument	Identity and parity agree on the examples but disagree at 2. Retain the ambiguity or supply the selection basis relevant to further use.
Treat an optimization formula as the learning algorithm	The chosen rule can remain unobtainable. Supply effective selection or update with its computational cost.
Use feedback that the agent never receives	A bandit update can silently assume labels for unchosen actions. Match the operation to the actual observation regime.
Keep eliminating candidates after the fixed-target premise fails	The threshold example leaves no survivor. Revise the treatment of noise or change and its result conditions.
Assume more data or computation always resolves the failure	An inadequate family or wrong target can survive both. Locate the changed dependency before choosing more work.