How can I use BigQuery ML to predict churn probability based on event streaming data collected from our web application via Segment?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How can I use BigQuery ML to predict churn probability based on event streaming data collected from our web application via Segment?
1 real search · 1 real-search pass
Google Cloud BigQuery ML logistic regression CREATE MODEL ML.PREDICT ML.EVALUATE documentation Segment BigQuery destination event schema documentationGoogle ↗
OBSERVEDInstruction
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.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
Google Cloud BigQuery ML logistic regression CREATE MODEL ML.PREDICT ML.EVALUATE documentation Segment BigQuery destination event schema documentation Google ↗
- Segment BigQuery destination documentation · segment.com
- BigQuery documentation · cloud.google.com
- Create an ML model in BigQuery ML by using SQL | Google Cloud Documentation · docs.cloud.google.com
- The CREATE MODEL statement for generalized linear models | BigQuery | Google Cloud Documentation · docs.cloud.google.com
- Build and use a classification model on census data | BigQuery | Google Cloud Documentation · docs.cloud.google.com
- BigQuery ML model evaluation overview | Google Cloud Documentation · docs.cloud.google.com
- The ML.PREDICT function | BigQuery | Google Cloud Documentation · docs.cloud.google.com
- BigQuery ML overview · cloud.google.com
- Training ML models · cloud.google.com
- Making predictions · cloud.google.com
- Segment documentation · segment.com