{"uid":"cap_iYm5KaD7ww5gsT7MKt2QH","slug":"feature-availability-numeric-distribution-and-iqr-outlier-flags-6c5d9850","name":"Feature Availability Numeric Distribution and IQR Outlier Flags","description":"Feature availability — Numeric distribution + IQR outlier flags: Numeric distribution + 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-10-080?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_TBALcmo52gUrXPqaXTEJH","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 IQR-based outlier flags for up to 200 feature availability records across two independent analytical reports.","exampleAgentPrompt":"Can you run a numeric distribution analysis and IQR outlier detection on my feature availability data? I have rows with populated_features and expected_features counts across two dataset groups from October 2026 — up to 200 rows — and I want both reports using the populated_features field.","exampleUseCases":[{"title":"ML pipeline feature completeness audit","prompt":"I have up to 150 records of daily feature availability from our ML pipeline, each with a row_id, dataset_group, date, populated_features, and expected_features. Can you compute the numeric distribution and flag IQR outliers on the populated_features column so I can see which days had abnormal feature population rates?"},{"title":"Data quality monitoring for dataset groups","prompt":"We track expected_features and populated_features across two dataset groups every day. Can you run the numeric distribution and IQR outlier analysis on these 80 rows using the expected_features field so we can spot which dataset groups consistently under-deliver on feature counts?"},{"title":"Ad-hoc feature availability anomaly detection","prompt":"I've got 200 rows of feature availability records with dates, row IDs, and both populated and expected feature counts. Please analyze the populated_features column for its distribution stats and flag any IQR outliers so I can investigate the worst offenders before our model retraining run."}],"resultDescription":"Returns two independent analytical reports: (1) a numeric distribution profile (e.g. min, max, mean, median, quartiles, std dev) and (2) IQR-based outlier flags per row for the selected field (populated_features or expected_features), covering up to 200 supplied rows.","failureModes":["Fewer than 1 row supplied returns an error","More than 200 rows causes rejection — batch must be split","Invalid date format (not YYYY-MM-DD) fails schema validation","Null values in the selected field may reduce report coverage or skew distribution","Missing required fields (row_id, date, populated_features, expected_features) cause validation failure","Payment not attached or insufficient USDC causes 402 rejection"],"whenToPreferThis":"Choose this endpoint when you have up to 200 feature availability records and need both a numeric distribution summary and IQR-based outlier detection in a single call for $0.01 USDC. It is ideal for lightweight, offline statistical profiling of dataset quality metrics without requiring a live data connection or external ML infrastructure. Prefer it over general-purpose analytics APIs when your input fits the structured row schema with populated_features and expected_features fields and you need two bundled reports cheaply.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:15:25.823Z","isFirstParty":false,"canonicalSlug":"feature-availability-numeric-distribution-and-iqr-outlier-flags-6c5d9850"}