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Label Review Analytics: IQR Outlier Flags + Two-Measure Correlation
x402
Label Review Analytics: IQR Outlier Flags + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Runs IQR-based outlier detection and two-measure Pearson/rank correlation analysis on label review records (reviewed_records and changed_labels) for up to 200 rows, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — IQR outlier flags + Two-measure correlation: IQR outlier flags + Two-measure correlation for supplied label review records (reviewed_records, changed_labels). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent analytical reports: (1) IQR outlier flags identifying which rows fall outside the interquartile range for the selected measure, and (2) a two-measure correlation report quantifying the statistical relationship between reviewed_records and changed_labels across the submitted rows.
Example use cases
Audit annotation team daily output
“I have daily logs from my annotation team — each row has a date, a dataset group name, how many records were reviewed, and how many labels were changed. Can you flag any days where the numbers look like outliers using IQR, and also tell me how correlated reviewed_records and changed_labels are across the whole dataset?”
Spot labeling pipeline anomalies
“We track reviewed_records and changed_labels per dataset group every day. Can you run an IQR outlier check and a correlation report on this batch of about 120 rows so we can catch any pipeline anomalies before our next model training run?”
QA check on crowdsourced label review
“I'm doing QA on a crowdsourced labeling project and I have up to 80 daily review records with dataset groups, reviewed counts, and changed label counts. Please run IQR outlier flagging and a two-measure correlation analysis so I can see which sessions are statistically unusual.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-176?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have structured label review tabular data (up to 200 rows) and need both IQR-based anomaly detection and a bivariate correlation analysis in a single low-cost call. It is ideal for ML/data-labeling quality audits where you want offline statistical analysis of reviewed_records vs changed_labels without any external lookups or predictions. Prefer alternatives if you need more than 200 rows, more than two measures correlated, or live/streaming data analysis.
Side effects
- Fewer than 1 row submitted — minItems validation error
- More than 200 rows submitted — maxItems validation error
- Missing required fields (row_id, date, reviewed_records, changed_labels) — schema validation failure
- Invalid date format (not YYYY-MM-DD) — pattern validation error
- Non-numeric values for reviewed_records or changed_labels — type validation error
- All values identical — correlation undefined or IQR of zero, may produce degenerate results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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