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Feature Availability IQR Outlier Flags + Date by Category Pivot Analytics
x402
Feature Availability IQR Outlier Flags + Date by Category Pivot Analytics is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Analyzes feature availability records to produce IQR-based outlier flags and a date-by-category pivot table for up to 200 rows of populated/expected feature data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot for supplied feature availability records (populated_features, expected_features). 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 identifying which rows have statistically anomalous values for the selected field (populated_features or expected_features), and (2) a date-by-category pivot table summarizing the selected field values grouped by date and dataset_group. Both reports are computed locally from the supplied rows (up to 200) with no external lookups.
Example use cases
Outlier detection in ML feature pipeline
“I have up to 150 rows of feature availability logs with populated_features and expected_features counts per dataset group and date — can you flag any IQR outliers in the populated_features column and also give me a pivot of values by date and category?”
Data quality audit across dataset groups
“Run an IQR outlier analysis and a date-by-group pivot on my expected_features records — I've got about 80 rows covering group-a and group-b from 2026-09-01 through 2026-09-30, and I want to spot any anomalies in the expected_features field.”
Feature completeness trend review
“Can you analyze these feature availability records — around 60 rows with dataset groups and daily dates — using IQR outlier flagging and a date-by-category pivot on the populated_features metric so I can see where completeness drops off?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-178?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both IQR-based outlier detection and a date-by-category pivot on feature availability data (populated_features or expected_features) in a single low-cost call. Ideal for data quality checks, ML pipeline monitoring, and feature completeness audits on batches up to 200 rows. Prefer this over general analytics tools when working specifically with dataset_group + date + feature count records and you want two complementary statistical views at once.
Side effects
- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, date, populated_features, expected_features) cause validation errors
- Malformed date strings not matching YYYY-MM-DD pattern cause schema rejection
- Insufficient data variance for meaningful IQR calculation if too few rows or all values are identical
- Null values in the selected numeric field may reduce outlier detection accuracy
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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