Quiz dotyczący wykrywania punktów zmiany

Odpowiedzi i wyjaśnienia pojawiają się w miarę rozwiązywania quizu.

1. Jakie rodzaje zachowań szeregowych może oznaczać punkt zmiany?
   • A shift in mean, variance, trend or distribution
   • Only a change in class labels
   • Only the first observation
   • A guaranteed causal intervention
2. Co ogólnie daje większa kara w przypadku ukaranego celu segmentacji offline?
   • Force a change at every observation
   • Discourage adding extra segments
   • Remove all segment costs
   • Increase the number of model variables
3. Co CUSUM gromadzi w czasie?
   • Only the single largest observation in the sequence
   • The number of free model parameters
   • Evidence of deviation from a reference process
   • The moving average of observations not yet collected
4. Które ustawienie jest szczególnie ważne dla zachowania alertów CUSUM?
   • The class encoding order
   • Liczba klastrów
   • The metric denominator
   • The control threshold
5. Co aktualizuje Bayesowskie wykrywanie punktów zmian online?
   • A single offline segmentation with no uncertainty
   • A probability distribution over run lengths since a change
   • The target's class labels
   • The model's seasonal period only
6. Why is a detected change not itself an explanation?
   • The algorithm always outputs a causal variable
   • Change points never correspond to real events
   • A statistical shift may come from process change, seasonality, drift or data issues
   • A segmentation has no time index
7. What risk can a very low segmentation penalty create?
   • Guaranteeing fewer false alarms
   • Making every series stationary
   • Removing autocorrelation by construction
   • Over-segmenting random noise
8. Which evaluation concern matters for repeated online monitoring?
   • False alarms and detection delay across repeated looks
   • Only one p-value at the final time
   • Whether all segment means are identical
   • The maximum number of predictors