{"uid":"cap_4bhWRZyYEaD54-J4yn4pu","slug":"feature-availability-numeric-interval-filter-iqr-outlier-flags-2072ad8f","name":"Feature Availability Numeric Interval Filter + IQR Outlier Flags","description":"Feature availability — Numeric interval filter + IQR outlier flags: Numeric interval filter + 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-130?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_RH9cVYiv7PeoJO710XE97","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":"Filters feature availability records by a numeric interval and flags IQR-based outliers across populated_features or expected_features, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Analyze my feature availability data for rows from group-a and group-b dated 2026-10-01 through 2026-10-04 — filter the populated_features field to only rows between 0 and 100, and flag any IQR outliers across all 4 rows.","exampleUseCases":[{"title":"Outlier detection in ML pipeline features","prompt":"I have daily feature availability logs across two dataset groups — can you filter the populated_features column to values between 5 and 50, and flag any IQR outliers in those 12 rows so I can clean up my training data?"},{"title":"Expected feature count range audit","prompt":"Check my expected_features records for the month of October 2026 — filter to the range 1 through 30 and tell me which of the 20 rows are statistical outliers based on IQR."},{"title":"Cross-group feature population anomaly check","prompt":"I've got 80 rows of feature availability data across four dataset groups and I need two things: a numeric interval filter on populated_features between -10 and 200, and IQR outlier flags so I can spot which groups are consistently abnormal."}],"resultDescription":"Returns two reports: (1) rows matching the specified numeric interval for the chosen field, and (2) IQR-based outlier flags indicating which rows fall outside the interquartile range — both computed locally against the supplied data with no external calls.","failureModes":["Missing required fields (row_id, date, populated_features, expected_features) cause validation errors","More than 200 rows submitted exceeds the per-call limit","Invalid date format (not YYYY-MM-DD) fails pattern validation","Minimum value greater than maximum value may produce empty interval results","Null values in the selected field may affect IQR computation","Payment not processed or insufficient USDC balance blocks the request"],"whenToPreferThis":"Choose this endpoint when you need simultaneous numeric range filtering and IQR outlier detection on feature availability records (populated_features / expected_features) in a single $0.01 call, without requiring any external lookups or live data connections. Ideal for data quality pipelines checking ML feature store completeness or flagging anomalous population counts across dataset groups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:34.215Z","isFirstParty":false,"canonicalSlug":"feature-availability-numeric-interval-filter-iqr-outlier-flags-2072ad8f"}