"Low cost" method is the key here, computationally simple on a spark/delta back end, as summary stats are compiled/updated automatically...won't require a re-index/shuffle.
Answer is B.
Monitoring summary statistics involves tracking basic statistical measures such as mean, median, variance, and standard deviation over time. By comparing these statistics between different time periods or datasets, you can detect significant changes that may indicate feature drift. Summary statistics trends also meets the simple and low-cost method requirements.
Monitoring summary statistics trends over time is a simple and low-cost method of monitoring numeric feature drift. It involves tracking basic statistical metrics such as mean, median, standard deviation, etc., and observing how they change over time. This method provides insights into whether the distribution of the feature values is shifting, which could indicate drift.
B. Summary statistics trends
Monitoring changes in summary statistics such as mean, median, standard deviation, and other relevant metrics over time can provide valuable insights into numeric feature drift. This method is simple, easy to implement, and does not require sophisticated statistical tests.
you're right, idk if it is consider "simple" and "low-cost" though
upvoted 1 times
...
...
Log in to ExamTopics
Sign in:
Community vote distribution
A (35%)
C (25%)
B (20%)
Other
Most Voted
A voting comment increases the vote count for the chosen answer by one.
Upvoting a comment with a selected answer will also increase the vote count towards that answer by one.
So if you see a comment that you already agree with, you can upvote it instead of posting a new comment.
karo3
3 months, 1 week ago64934ca
4 months, 3 weeks ago03355a2
5 months agosindhu_gowda
5 months, 2 weeks agohugodscarvalho
10 months agoGVR76
10 months, 3 weeks agoStevenTan
10 months, 3 weeks agoThoBustos
6 months, 1 week ago