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Feature Availability Analytics — Two-Measure Correlation + Explicit Missing-Value Fill
x402
Feature Availability Analytics — Two-Measure Correlation + 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 2026-10-19).
Computes Pearson correlation between populated_features and expected_features, and fills missing values in a chosen field using zero, mean, or median imputation — delivered as two separate analytical reports for up to 200 feature availability rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Two-measure correlation + Explicit missing-value fill: Two-measure correlation + Explicit missing-value fill 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 analytical reports: (1) a Pearson correlation result between populated_features and expected_features across all supplied rows, and (2) a missing-value fill report showing imputed values for the chosen field (populated_features or expected_features) using the specified method (zero, mean, or median). All processing is local with no external lookups. Supports up to 200 rows per call.
Example use cases
ML pipeline feature completeness audit
“I've got daily feature availability records for two dataset groups over the past month — can you correlate populated_features against expected_features and fill any nulls in populated_features with the median, so I can see how complete my training data is?”
Data quality report for feature store
“Run two analytics reports on my feature store rows: first show me how correlated the populated vs expected feature counts are, then fill in any missing expected_features values using the mean. Here are my 150 rows with row_id, date, dataset_group, populated_features, and expected_features.”
Zero-fill missing features before model training
“Before I train my model, I need to fill missing values in expected_features with zero and also check how well populated_features correlates with expected_features across my 80 dataset rows — can you give me both reports?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-184?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a correlation analysis and a missing-value fill report on feature availability data in a single $0.01 USDC call. It is purpose-built for populated_features vs expected_features datasets with up to 200 rows, requires no external data sources, and delivers two independent reports from shared local analytical engines. Prefer it over general-purpose statistics APIs when working with feature store completeness data and needing a cheap, fast, offline computation with explicit imputation control.
Side effects
- Fewer than 1 row supplied — returns validation error
- More than 200 rows supplied — rejected with maxItems constraint error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Unsupported fill method value outside enum [zero, mean, median] — rejected
- All values in one measure are null — correlation computation may return NaN or error
- Numeric values outside allowed range (±1 trillion) — rejected by schema
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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