Idanwo A/B fun adanwo Awọn awoṣe ML

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Kini idi ti awọn ẹgbẹ A/B ṣe idanwo awọn awoṣe ni iṣelọpọ dipo igbẹkẹle awọn metiriki aisinipo?
   • Awoṣe pẹlu deede aisinipo to dara julọ tun le ṣe ipalara awọn metiriki iṣowo gidi
   • Awọn metiriki aisinipo jẹ arufin
   • Ṣiṣejade jẹ din owo ju idanwo aisinipo lọ
   • Awọn data aisinipo ko si
2. Ipa wo ni iṣẹ iyansilẹ laileto ṣe ninu idanwo A/B?
   • O jẹ ki idanwo naa yarayara
   • It supports balance between groups in expectation, subject to design and implementation assumptions
   • O npa awọn olumulo alaiṣedeede kuro
   • O dinku nọmba awọn awoṣe ti o nilo
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. Kini idi ti itupalẹ agbara ṣaaju idanwo A/B?
   • Lati mu aami kan fun idanwo naa
   • Lati ṣe iṣiro iye awọn olumulo ni o nilo lati rii igbẹkẹle gidi ipa kan
   • Lati encrypt awọn ijabọ
   • Lati pa ẹgbẹ iṣakoso rẹ
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