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Label Review Analytics: IQR Outlier Flags + Missing-Value Fill
x402
Label Review Analytics: IQR Outlier Flags + 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).
Analyzes label review records (reviewed_records, changed_labels) to flag IQR-based outliers and fill missing values using a chosen imputation method, returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + 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) an IQR-based outlier flag report identifying which rows have values outside the interquartile range for the specified field, and (2) a missing-value fill report showing imputed values for null entries using the chosen method (zero, mean, or median). Both reports cover the same supplied dataset of up to 200 rows.
Example use cases
Annotation pipeline quality audit
“I have 150 rows of label review records across two dataset groups from last quarter — can you flag any IQR outliers and fill missing reviewed_records values using median so I can clean the data before retraining?”
Catching mislabeled batch anomalies
“Run an IQR outlier check on my changed_labels column and use zero-fill for any nulls — here are the daily label review rows for group-a and group-b from October, I want to spot any batches that look suspiciously high or low.”
Pre-model data validation for labeling team
“Before I hand this annotation dataset off to the modeling team, can you give me two reports — one flagging IQR outliers and one filling missing values with the mean — for the reviewed_records field across these 80 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-180?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both IQR-based outlier detection and missing-value imputation on label review data (reviewed_records and changed_labels) in a single low-cost call. It is ideal for ML data preparation workflows where annotation quality needs to be validated and cleaned before model training. Prefer this over general-purpose analytics tools when your data is structured around dataset_group and date-stamped label review counts and you want two reports in one $0.01 call without any external lookups or predictions.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, reviewed_records, changed_labels) cause validation error
- Invalid date format (not YYYY-MM-DD) triggers pattern mismatch error
- Numeric values outside the ±1 trillion range cause schema validation failure
- Empty rows array returns an error due to minItems:1 constraint
- Invalid method enum value (not zero/mean/median) causes rejection
- Invalid field enum value (not reviewed_records/changed_labels) causes rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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