Testare A/B pentru modele ML Test

Răspunsurile și explicațiile apar pe măsură ce susțineți testul.

1. De ce echipele A/B testează modele în producție în loc să aibă încredere în valorile offline?
   • Un model cu o mai bună acuratețe offline poate afecta în continuare valorile reale de afaceri
   • Valorile offline sunt ilegale
   • Producția este mai ieftină decât testarea offline
   • Datele offline nu există
2. Ce rol joacă repartizarea aleatorie într-un test A/B?
   • Face testul să ruleze mai repede
   • It supports balance between groups in expectation, subject to design and implementation assumptions
   • Acesta șterge utilizatorii părtinitori
   • Reduce numărul de modele necesare
3. In a model A/B test, what usually happens in the treatment group?
   • The candidate scores requests but all outputs are withheld
   • Every user is assigned to both models for the same decision
   • The candidate model serves user-facing decisions for the randomly assigned group
   • The control model is removed before any comparison
4. Under a null hypothesis and stated test assumptions, how is a p-value interpreted?
   • As the probability that the null hypothesis is true
   • As the probability that the treatment caused the observed result
   • As the probability that the experiment will replicate
   • As the probability of data at least this extreme if the null hypothesis holds
5. How can a multi-armed bandit change an experiment while it runs?
   • It can allocate more traffic to variants with better observed outcomes and less to others
   • It keeps every variant at a fixed equal allocation throughout
   • It removes randomization from every assignment
   • It guarantees the best variant is identified without uncertainty
6. Care este scopul unei analize de putere înainte de un test A/B?
   • Pentru a alege un logo pentru experiment
   • Pentru a estima câți utilizatori sunt necesari pentru a detecta în mod fiabil un efect real
   • Pentru a cripta traficul
   • Pentru a șterge grupul de control
7. What does a sample-ratio mismatch (SRM) signal during an A/B test?
   • The treatment model is necessarily better
   • The experiment has sufficient statistical power
   • Observed assignment counts differ materially from the configured allocation, which can indicate a data or randomization problem
   • The primary metric improved causally
8. Why include guardrail metrics alongside an experiment’s primary success metric?
   • To guarantee the candidate wins on every metric
   • To replace the primary metric after results are observed
   • To avoid defining a decision rule before the test
   • To detect meaningful harms in other outcomes even when the primary metric improves