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Label Review Analytics — Category Frequencies + IQR Outlier Flags
x402
Label Review Analytics — Category Frequencies + IQR Outlier Flags 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).
Computes category-level frequency distributions and IQR-based outlier flags for label review records (reviewed_records and changed_labels) on up to 200 supplied rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Category frequencies + IQR outlier flags: Category frequencies + IQR outlier flags 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 analytical reports: (1) category frequency counts broken down by dataset_group for the chosen field, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range for the selected field (reviewed_records or changed_labels). Results cover all supplied rows up to 200.
Example use cases
Annotation pipeline quality audit
“I have a batch of 120 label review records across groups A, B, and C with daily reviewed_records and changed_labels counts — can you run category frequency analysis and flag any IQR outliers in the changed_labels field so I can spot where reviewers are making unusually many corrections?”
Dataset group distribution check
“Here are 80 rows from our labeling platform with dataset_group, date, reviewed_records, and changed_labels — can you break down the category frequencies per group and identify which rows are IQR outliers in reviewed_records?”
Weekly label review anomaly detection
“I want to sanity-check last week's label review data — 60 rows spanning three dataset groups — can you compute the category frequencies and use IQR to flag any rows where reviewed_records look anomalous?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-107?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both category frequency analysis and IQR outlier detection on label review data in a single call. It is specifically designed for annotation/labeling QA workflows tracking reviewed_records and changed_labels metrics. Prefer it over general-purpose analytics when you have up to 200 structured label review rows and want two coordinated statistical reports without external lookups or ML inference.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, dataset_group, reviewed_records, changed_labels) cause validation errors
- Invalid date format (non-YYYY-MM-DD) triggers schema validation failure
- Numeric values outside allowed range (-1T to +1T) cause rejection
- Insufficient data points for meaningful IQR computation (e.g. single row)
- Payment failure if USDC balance is insufficient for the $0.01 fee
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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