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Label Review Analytics — Base-100 Series + Explicit Missing-Value Fill
x402
Label Review Analytics — Base-100 Series + 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 2026-10-15).
Computes a base-100 index series and fills missing values (zero/mean/median) for label review records, returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Base-100 series + Explicit missing-value fill: Base-100 series + 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 independent analytical reports: (1) a base-100 index series normalizing the chosen field relative to the specified base row, and (2) the same series with missing values explicitly filled using the chosen method (zero, mean, or median). Both reports cover all supplied rows (up to 200).
Example use cases
Normalizing annotation throughput over time
“I have 30 days of label review data across two dataset groups and want to see how reviewed_records trend relative to day 0 as a base-100 index — fill any missing days with the median and give me both reports.”
Detecting label churn spikes in ML datasets
“Can you analyze my changed_labels data for the past quarter using a base-100 series starting from the first row, filling missing values with zero, so I can spot which weeks had unusual label churn?”
Comparing group-level review performance
“I've got label review records for groups A, B, and C over 90 rows — run a base-100 normalization on reviewed_records with mean imputation for missing values and set the baseline to row index 0, so I can compare group performance fairly.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-169?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a base-100 normalized time series and explicit missing-value imputation for label review or annotation quality metrics in a single call. It is ideal for offline/batch analysis of up to 200 rows of structured review data without requiring live data lookup or external predictions. Prefer it over generic analytics tools when your data has the reviewed_records/changed_labels schema and you want two complementary reports in one low-cost call.
Side effects
- More than 200 rows submitted — request rejected with validation error
- Invalid date format (not YYYY-MM-DD) — row fails schema validation
- base_index out of range (>=number of rows) — computation error or fallback
- All values null for selected field — mean/median fill may produce NaN or error
- Missing required fields (row_id, date, reviewed_records, changed_labels) — row rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| base_index | integer |
Tags
x402
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