Awoṣe Drift erin adanwo

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

1. Kini iyato laarin data fiseete ati Erongba fiseete?
   • Data fiseete ayipada awọn igbewọle 'pinpin; fiseete ero ṣe ayipada ibatan igbewọle-si-jade
   • Wọn jẹ orukọ meji fun ohun kanna
   • Data fiseete nikan ni ipa lori awọn aworan; fiseete ero nikan ni ipa lori ọrọ
   • Data fiseete ṣẹlẹ ni ikẹkọ; fiseete ero ṣẹlẹ ni igbeyewo
2. How should a team interpret a PSI value such as 0.30?
   • As a guarantee that model accuracy has fallen
   • As a signal to investigate under the chosen monitoring policy, not a universal proof of harmful drift
   • As an identical threshold for every feature and population
   • As proof that retraining must begin immediately
3. Kini idi ti iṣawari fiseete ti o da lori titẹ sii le funni ni ikilọ kutukutu?
   • Nitori awọn igbewọle nigbagbogbo jẹ aṣiṣe akọkọ
   • Nitori awọn igbewọle jẹ awọn faili kekere
   • Nitori awọn aami-otitọ ilẹ nigbagbogbo de pupọ nigbamii ju awọn asọtẹlẹ lọ
   • Nitoripe awọn awoṣe foju kọ awọn akole patapata
4. What does a two-sample Kolmogorov-Smirnov test compare?
   • The average accuracy of two classifiers only
   • The class labels predicted by a single model
   • The parameter values of two neural networks
   • The empirical cumulative distribution functions of two samples
5. Oju iṣẹlẹ wo ni apẹẹrẹ ti o han gbangba ti fiseete ero?
   • Ohun ti awọn olumulo ro pe 'spam' yipada lori akoko paapaa fun awọn imeeli ti o jọra
   • Kamẹra ṣe agbejade awọn aworan didan diẹ
   • Awọn ọjọ-ori alabara ninu dataset jẹ kanna
   • Atokọ data ti wa ni ipamọ ni ọna kika faili titun kan
6. What is a safe first response when an input-drift monitor triggers?
   • Automatically deploy a newly retrained model without evaluation
   • Investigate the data and impact, then decide whether retraining or another action is justified
   • Delete the reference data
   • Assume every shift is harmful concept drift
7. What can TensorFlow Data Validation check in an ML pipeline?
   • The causal effect of a feature
   • Whether every model prediction is correct
   • Data schemas and statistics, including unexpected changes
   • The absolute accuracy of an unlabeled dataset
8. What does the Population Stability Index (PSI) measure in a drift-monitoring setup?
   • The model’s accuracy on labeled examples
   • A causal effect of an input feature
   • A universal pass/fail threshold for all models
   • A numerical summary of how binned feature distributions differ between reference and current data