{"uid":"cap_CsakPwhTU8oZ_FvkJrfdB","slug":"feature-availability-iqr-outlier-flags-two-measure-correlation-5a08a6f3","name":"Feature Availability IQR Outlier Flags + Two-Measure Correlation Analytics","description":"Feature availability — IQR outlier flags + Two-measure correlation: IQR outlier flags + Two-measure correlation 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-10-176?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_s64nSux8m9zC23UjcxZyP","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":"Runs IQR-based outlier detection and two-measure correlation analysis on supplied feature availability data (populated_features vs expected_features) for up to 200 rows.","exampleAgentPrompt":"Can you run IQR outlier flagging and two-measure correlation on my feature availability records? I have rows with populated_features and expected_features values across groups like 'train' and 'test' from 2024-01-01 to 2024-01-10, and I want to analyze the populated_features field.","exampleUseCases":[{"title":"ML pipeline feature completeness audit","prompt":"I have 150 rows of daily feature availability data from my ML pipeline with populated_features and expected_features counts per dataset_group. Can you flag any IQR outliers and also tell me how correlated those two measures are?"},{"title":"Data quality monitoring across dataset groups","prompt":"I want to check if any of my dataset groups have abnormal feature population rates — here are my records with row_id, date, dataset_group, populated_features, and expected_features for October. Run the IQR outlier flags and correlation analysis on expected_features."},{"title":"Investigating feature drift between train and test sets","prompt":"My train and test dataset groups have been logging populated_features and expected_features daily. Can you run the IQR outlier detection plus the two-measure correlation report on this data to see if anything looks off?"}],"resultDescription":"Returns two statistical reports: (1) IQR outlier flags identifying which rows have anomalous values for the selected field based on interquartile range analysis, and (2) a two-measure correlation report quantifying the relationship between populated_features and expected_features across all supplied rows.","failureModes":["Input array is empty or missing required fields (row_id, date, populated_features, expected_features)","Date values not matching YYYY-MM-DD format pattern","More than 200 rows submitted exceeding the per-call limit","Null values in both measure fields preventing correlation computation","Payment of 0.01 USDC not completed via x402 protocol"],"whenToPreferThis":"Choose this endpoint when you need both IQR-based outlier detection and correlation analysis on feature availability data (populated vs expected features) in a single low-cost call. Ideal for data quality checks on ML dataset feature completeness metrics where you supply your own data and need local statistical analysis without any external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:47:01.139Z","isFirstParty":false,"canonicalSlug":"feature-availability-iqr-outlier-flags-two-measure-correlation-5a08a6f3"}