{"uid":"cap_z-Ow013UxHQgiHP6fq54x","slug":"feature-availability-analytics-two-measure-correlation-explicit-6f58b5be","name":"Feature Availability Analytics — Two-Measure Correlation + Explicit Missing-Value Fill","description":"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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-184?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_-_-mxlnpI7OoHPjKjLeHV","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 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.","exampleAgentPrompt":"I have up to 200 rows of feature availability data with populated_features and expected_features columns — can you run a correlation analysis between those two measures and also fill any missing values in the populated_features field using median imputation?","exampleUseCases":[{"title":"ML pipeline feature completeness audit","prompt":"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?"},{"title":"Data quality report for feature store","prompt":"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."},{"title":"Zero-fill missing features before model training","prompt":"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?"}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:56.431Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-two-measure-correlation-explicit-6f58b5be"}