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Label Review Analytics: IQR Outlier Flags + Secondary-Measure Weighted Mean
x402
Label Review Analytics: IQR Outlier Flags + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Runs two local analytical reports on label review data: IQR-based outlier detection on a chosen field and a secondary-measure weighted mean, for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — IQR outlier flags + Secondary-measure weighted mean: IQR outlier flags + Secondary-measure weighted mean 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 indicating which rows fall outside the interquartile range for the selected field, and (2) a secondary-measure weighted mean computed across the supplied rows. Both reports are derived entirely from the submitted data with no external lookups.
Example use cases
Annotation quality audit by group
“I have 80 label review records across several dataset groups with dates, reviewed_records counts, and changed_labels counts. Can you flag any IQR outliers and compute the weighted mean for reviewed_records so I can spot inconsistent annotators?”
Changed labels anomaly detection
“Run the IQR outlier detection and weighted mean reports on my changed_labels data — I've got about 120 rows of daily label review logs across two groups and I want to know which days had abnormally high label changes.”
Pre-release dataset review sanity check
“Before we ship this dataset, can you analyze my 30 label review rows for the changed_labels field — flag any outliers using IQR and give me a weighted mean so I can confirm the labeling was consistent?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-177?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, local statistical quality checks on label review data — specifically IQR-based outlier detection combined with a weighted mean — without any external data lookup, prediction, or professional assessment. Ideal for ML annotation pipelines, data quality audits, or labeling consistency reviews on small-to-medium batches (up to 200 rows) at minimal cost ($0.01 USDC for both reports).
Side effects
- Fewer than 1 row submitted — returns validation error
- More than 200 rows submitted — request rejected due to row limit
- Invalid date format (non-YYYY-MM-DD) — schema validation failure
- Null or missing required fields (row_id, reviewed_records, changed_labels) — error response
- Invalid field enum value — rejected at schema level
- Numeric values outside allowed range — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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