{"uid":"cap_9DPM2yVzLTX5M596WCsO4","slug":"label-review-analytics-category-frequencies-iqr-outlier-flags-e86e1886","name":"Label Review Analytics — Category Frequencies + IQR Outlier Flags","description":"Label review — Category frequencies + IQR outlier flags: Category frequencies + IQR outlier flags for supplied label review records (reviewed_records, changed_labels). 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-12-107?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","reviewed_records","changed_labels"],"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},"changed_labels":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"reviewed_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","changed_labels":1,"reviewed_records":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","changed_labels":2,"reviewed_records":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","changed_labels":3,"reviewed_records":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","changed_labels":4,"reviewed_records":30}],"maxItems":200,"minItems":1},"field":{"enum":["reviewed_records","changed_labels"],"type":"string","default":"reviewed_records"}}},"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_nSCw9U-ijMvD2MLhMxQMs","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 category-level frequency distributions and IQR-based outlier flags for label review records (reviewed_records and changed_labels) on up to 200 supplied rows.","exampleAgentPrompt":"Can you analyze this label review dataset — I have 50 rows with date, dataset_group, reviewed_records, and changed_labels fields — and give me the category frequencies plus flag any IQR outliers in the reviewed_records column?","exampleUseCases":[{"title":"Annotation pipeline quality audit","prompt":"I have a batch of 120 label review records across groups A, B, and C with daily reviewed_records and changed_labels counts — can you run category frequency analysis and flag any IQR outliers in the changed_labels field so I can spot where reviewers are making unusually many corrections?"},{"title":"Dataset group distribution check","prompt":"Here are 80 rows from our labeling platform with dataset_group, date, reviewed_records, and changed_labels — can you break down the category frequencies per group and identify which rows are IQR outliers in reviewed_records?"},{"title":"Weekly label review anomaly detection","prompt":"I want to sanity-check last week's label review data — 60 rows spanning three dataset groups — can you compute the category frequencies and use IQR to flag any rows where reviewed_records look anomalous?"}],"resultDescription":"Returns two analytical reports: (1) category frequency counts broken down by dataset_group for the chosen field, and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range for the selected field (reviewed_records or changed_labels). Results cover all supplied rows up to 200.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, date, dataset_group, reviewed_records, changed_labels) cause validation errors","Invalid date format (non-YYYY-MM-DD) triggers schema validation failure","Numeric values outside allowed range (-1T to +1T) cause rejection","Insufficient data points for meaningful IQR computation (e.g. single row)","Payment failure if USDC balance is insufficient for the $0.01 fee"],"whenToPreferThis":"Choose this endpoint when you need both category frequency analysis and IQR outlier detection on label review data in a single call. It is specifically designed for annotation/labeling QA workflows tracking reviewed_records and changed_labels metrics. Prefer it over general-purpose analytics when you have up to 200 structured label review rows and want two coordinated statistical reports without external lookups or ML inference.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:49.706Z","isFirstParty":false,"canonicalSlug":"label-review-analytics-category-frequencies-iqr-outlier-flags-e86e1886"}