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Feature Availability Analytics: Numeric Distribution + Two-Measure Correlation
x402
Feature Availability Analytics: Numeric Distribution + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).
Computes numeric distribution statistics and Pearson correlation between populated_features and expected_features for up to 200 supplied feature-availability records in two independent reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Numeric distribution + Two-measure correlation: Numeric distribution + Two-measure correlation 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) a numeric distribution report for the selected field (populated_features or expected_features) covering summary stats such as mean, median, variance, and histogram-style breakdown; and (2) a two-measure correlation report quantifying the relationship between populated_features and expected_features across the supplied rows. No external data is fetched; all computation runs on the supplied rows only.
Example use cases
Feature completeness audit across dataset groups
“I have a dataset with populated_features and expected_features counts across group-a and group-b over the past week — can you run a distribution analysis on populated_features and also check how correlated it is with expected_features? I'll give you up to 50 rows.”
ML pipeline feature coverage monitoring
“We track how many features are expected vs actually populated each day for our ML models. Can you compute the numeric distribution of expected_features and show me the correlation between populated_features and expected_features across these 120 daily records?”
Data quality reporting for stakeholders
“I need a quick statistical summary for our data quality report — specifically the distribution of populated_features values and their correlation with expected_features across these dataset groups and dates. There are about 80 rows total.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-081?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw feature-availability records (populated_features, expected_features) and need both a distribution summary and a correlation report in a single low-cost call without any external data lookup or ML prediction. It is ideal for data quality audits, ML pipeline monitoring, and completeness reporting on small-to-medium datasets (≤200 rows). Prefer alternatives if you need live data enrichment, predictions, or analysis of fields beyond these two measures.
Side effects
- More than 200 rows supplied — request rejected or truncated
- Missing required fields (row_id, date, populated_features, expected_features) cause validation errors
- Date strings not matching YYYY-MM-DD pattern fail schema validation
- All null values in numeric fields result in degenerate or undefined statistical outputs
- Insufficient variance in data may produce correlation values of NaN or ±1 with low reliability
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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