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Label Review Analytics: Secondary-Measure Weighted Mean + Explicit Missing-Value Fill
x402
Label Review Analytics: Secondary-Measure Weighted Mean + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-03).
Computes a secondary-measure weighted mean and fills missing values explicitly across label review records (reviewed_records, changed_labels) for up to 200 rows in two bundled reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + Explicit missing-value fill 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) a secondary-measure weighted mean of the target field (reviewed_records or changed_labels) grouped by dataset_group, and (2) an explicit missing-value fill of null entries using the chosen strategy (zero, mean, or median). All computations are performed locally on the supplied data with no external lookups.
Example use cases
ML annotation QA aggregation
“I have daily label review records for two dataset groups over the past month — each row has a date, a group name, reviewed_records count, and changed_labels count. Can you compute the weighted mean of changed_labels using reviewed_records as the weight, and fill any nulls using median imputation? Here are my 40 rows.”
Missing-value imputation for review metrics
“My label review export has some null entries in the reviewed_records column. Can you fill those gaps using zero-fill and also give me the weighted mean of reviewed_records across all my dataset groups? I'll provide about 80 rows.”
Cross-group label change rate analysis
“I want to compare changed_labels rates across my annotation groups — can you compute a secondary-measure weighted mean of changed_labels (weighted by reviewed_records) and handle any missing values with mean imputation across my 120 label review records organized by group and date?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-187?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a bundled two-report analysis (weighted mean + missing-value fill) specifically for label review datasets with reviewed_records and changed_labels fields. It is ideal for ML/annotation QA workflows where you have up to 200 rows and want both aggregation and imputation in a single low-cost call. Prefer alternatives if you need live data lookup, prediction, or more than 200 rows.
Side effects
- More than 200 rows submitted — API rejects with a validation error
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Missing required fields (row_id, reviewed_records, changed_labels) result in row rejection
- All values null for a group may produce undefined weighted mean
- Invalid enum value for 'method' or 'field' causes request rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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