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Feature Availability Analytics — Category Frequencies + IQR Outlier Flags
x402
Feature Availability Analytics — Category Frequencies + IQR Outlier Flags 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).
Computes category-level frequency distributions and IQR-based outlier flags for up to 200 feature availability records (populated_features or expected_features), returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Category frequencies + IQR outlier flags: Category frequencies + IQR outlier flags 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
Two independent analytical reports: (1) a category frequency table showing how often each dataset_group value appears in the supplied rows, and (2) IQR-based outlier flags identifying which rows have values outside the interquartile range for the selected field (populated_features or expected_features). Results are computed locally from the supplied data with no external lookups.
Example use cases
ML pipeline feature completeness audit
“I have 120 rows of feature availability logs from my ML pipeline, each tagged with a dataset_group and date, and I want to know which groups appear most often and whether any rows have wildly abnormal populated_features values based on IQR — can you run both reports?”
Data quality check before model training
“Before I kick off training, can you check my feature availability dataset — about 80 rows with populated_features and expected_features — to spot any IQR outliers in expected_features and show me the frequency breakdown by dataset_group?”
Daily dataset coverage monitoring
“I'm tracking feature availability daily across two dataset groups, group-a and group-b, and I want to see how frequently each group appears in my 200-row log and flag any unusual spikes or drops in populated_features using IQR outlier detection.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-107?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both category frequency analysis and IQR outlier detection on feature availability data in a single paid call. It is well-suited for small-to-medium datasets (up to 200 rows) where you want offline, deterministic statistical analysis without any external API calls, ML predictions, or professional assessments. Prefer it over general analytics services when you specifically have populated_features and expected_features columns and want grouped frequency counts alongside interquartile-range-based anomaly flags in one response.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, populated_features, expected_features) cause validation errors
- Invalid date format (non YYYY-MM-DD) triggers schema rejection
- Null values in the target numeric field may affect IQR computation
- Insufficient data variance (e.g. all identical values) may produce degenerate IQR results
- Payment of 0.01 USDC not included or rejected causes 402 error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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