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Label Review Analytics: IQR Outlier Flags + Keyed Dataset Comparison
x402
Label Review Analytics: IQR Outlier Flags + Keyed Dataset Comparison 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 two analytical reports on label review data: IQR-based outlier detection and a keyed comparison between two datasets, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — IQR outlier flags + Keyed dataset comparison: IQR outlier flags + Keyed dataset comparison 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
Two independent analytical reports: (1) IQR outlier flags identifying which rows fall outside the interquartile range for the selected field, and (2) a keyed comparison between the primary and reference datasets highlighting differences by row_id and dataset_group. No external data is fetched; all analysis uses supplied data only.
Example use cases
Annotation pipeline quality audit
“I have two batches of label review records from last month — each up to 200 rows with row_id, dataset_group, date, reviewed_records, and changed_labels. Can you run IQR outlier flagging on reviewed_records and compare the two batches by key to find discrepancies?”
Detecting unusual changed_labels spikes
“Here are my daily label review records for October — 150 rows with dataset_group, date, and changed_labels values. Flag any rows where changed_labels looks like an IQR outlier, and compare against my baseline dataset for the same period.”
Comparing annotator group performance
“I want to compare group-a and group-b label review records side by side — I have two datasets of about 80 rows each with row_id, date, reviewed_records, and changed_labels. Run the keyed comparison and flag outliers in reviewed_records so I can spot which groups are behaving abnormally.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-179?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined IQR outlier detection and keyed dataset comparison specifically for label review records (reviewed_records, changed_labels) in a single low-cost call. Ideal for ML annotation pipelines, data quality audits, and comparing annotation batches without any external lookups or live data requirements. Best for datasets under 200 rows where local statistical analysis suffices.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, reviewed_records, changed_labels) causes rejection
- Invalid date format (non YYYY-MM-DD) fails schema validation
- Null values in the selected analysis field may affect IQR computation
- Mismatched keys between primary and comparison datasets yield empty join results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| other | array |
Tags
x402
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