{"uid":"cap_AdUZJdwh-3DjWG_ixiROt","slug":"label-review-analytics-iqr-outlier-flags-missing-value-fill-2a2441bb","name":"Label Review Analytics: IQR Outlier Flags + Missing-Value Fill","description":"Label review — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill 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-12-180?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"},"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_fIwFpB6pWROBG5ejHMebi","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":"Analyzes label review records (reviewed_records, changed_labels) to flag IQR-based outliers and fill missing values using a chosen imputation method, returning two analytical reports.","exampleAgentPrompt":"Can you run IQR outlier detection and fill any missing values using median imputation on my label review data — the field I care about is reviewed_records — here are up to 200 rows with row_id, date, dataset_group, reviewed_records, and changed_labels?","exampleUseCases":[{"title":"Annotation pipeline quality audit","prompt":"I have 150 rows of label review records across two dataset groups from last quarter — can you flag any IQR outliers and fill missing reviewed_records values using median so I can clean the data before retraining?"},{"title":"Catching mislabeled batch anomalies","prompt":"Run an IQR outlier check on my changed_labels column and use zero-fill for any nulls — here are the daily label review rows for group-a and group-b from October, I want to spot any batches that look suspiciously high or low."},{"title":"Pre-model data validation for labeling team","prompt":"Before I hand this annotation dataset off to the modeling team, can you give me two reports — one flagging IQR outliers and one filling missing values with the mean — for the reviewed_records field across these 80 rows?"}],"resultDescription":"Returns two independent analytical reports: (1) an IQR-based outlier flag report identifying which rows have values outside the interquartile range for the specified field, and (2) a missing-value fill report showing imputed values for null entries using the chosen method (zero, mean, or median). Both reports cover the same supplied dataset of up to 200 rows.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Missing required fields (row_id, date, reviewed_records, changed_labels) cause validation error","Invalid date format (not YYYY-MM-DD) triggers pattern mismatch error","Numeric values outside the ±1 trillion range cause schema validation failure","Empty rows array returns an error due to minItems:1 constraint","Invalid method enum value (not zero/mean/median) causes rejection","Invalid field enum value (not reviewed_records/changed_labels) causes rejection"],"whenToPreferThis":"Choose this endpoint when you need both IQR-based outlier detection and missing-value imputation on label review data (reviewed_records and changed_labels) in a single low-cost call. It is ideal for ML data preparation workflows where annotation quality needs to be validated and cleaned before model training. Prefer this over general-purpose analytics tools when your data is structured around dataset_group and date-stamped label review counts and you want two reports in one $0.01 call without any external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:37:40.753Z","isFirstParty":false,"canonicalSlug":"label-review-analytics-iqr-outlier-flags-missing-value-fill-2a2441bb"}