We are getting too many false positives from our Great Expectations data tests; how do I adjust the expectation parameters to be more forgiving of normal data drift?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
We are getting too many false positives from our Great Expectations data tests; how do I adjust the expectation parameters to be more forgiving of normal data drift?
1 real search · 1 real-search pass
Great Expectations documentation mostly parameter strict_min strict_max expect_column_mean_to_be_between unexpected_percentiles data drift expectationsGoogle ↗
OBSERVED
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
Great Expectations documentation mostly parameter strict_min strict_max expect_column_mean_to_be_between unexpected_percentiles data drift expectations Google ↗
- GX distribution validation guide · docs.greatexpectations.io
- Create an Expectation · docs.greatexpectations.io
- Create an Expectation · docs.greatexpectations.io