{"uid":"cap_FrdFHANzV1gUIQvoBcV-Z","slug":"feature-availability-analytics-numeric-distribution-two-measure-f8ee067e","name":"Feature Availability Analytics: Numeric Distribution + Two-Measure Correlation","description":"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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-081?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","populated_features","expected_features"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_features":1,"populated_features":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_features":2,"populated_features":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":30}],"maxItems":200,"minItems":1},"field":{"enum":["populated_features","expected_features"],"type":"string","default":"populated_features"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_m4FOSBbu967UY9Ufz-Asn","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"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.","exampleAgentPrompt":"Can you run a numeric distribution and correlation analysis on my feature availability data? I have 4 rows across two groups — group-a and group-b — with dates from 2026-10-01 to 2026-10-04, and I want to analyze the populated_features field against expected_features.","exampleUseCases":[{"title":"Feature completeness audit across dataset groups","prompt":"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."},{"title":"ML pipeline feature coverage monitoring","prompt":"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?"},{"title":"Data quality reporting for stakeholders","prompt":"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."}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:42.462Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-numeric-distribution-two-measure-f8ee067e"}