{"uid":"cap_1oRxdbf17t4yhUVeXopl1","slug":"feature-availability-analytics-weighted-mean-keyed-dataset-comparison-6f21eaea","name":"Feature Availability Analytics — Weighted Mean & Keyed Dataset Comparison","description":"Feature availability — Secondary-measure weighted mean + Keyed dataset comparison: Secondary-measure weighted mean + Keyed dataset comparison 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-10-186?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}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_features":1,"populated_features":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_features":2,"populated_features":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":30}],"maxItems":200,"minItems":1},"field":{"enum":["populated_features","expected_features"],"type":"string","default":"populated_features"},"other":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","populated_features","expected_features"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","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":21},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":17},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":33}],"maxItems":200,"minItems":0}}},"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_0OGx4G_AoL_bmcO0HLi6r","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 a secondary-measure weighted mean and performs a keyed dataset comparison on supplied feature availability records (populated_features vs expected_features) for up to 200 rows, returning two independent analytical reports.","exampleAgentPrompt":"I have two sets of feature availability records — each with populated_features and expected_features per row, grouped by dataset_group and date — can you run a secondary-measure weighted mean on the populated_features field and also compare the two datasets side by side to see where they differ?","exampleUseCases":[{"title":"Feature completeness audit across dataset groups","prompt":"I have 150 rows of feature availability data across two groups — 'model-a' and 'model-b' — with populated_features and expected_features per date. Can you compute a weighted mean of populated_features and compare the two groups to see which has better feature coverage?"},{"title":"Comparing feature fill rates between two dataset snapshots","prompt":"I've got two batches of feature availability records from different pipeline runs, each with row_id, date, dataset_group, populated_features, and expected_features. Run a weighted mean on expected_features and compare the two batches to highlight where the fill rates diverge."},{"title":"Data quality reporting for ML feature pipeline","prompt":"My ML feature pipeline produces daily records with populated_features and expected_features per dataset_group. Can you compute the secondary-measure weighted mean on populated_features across up to 80 rows and give me a keyed comparison against a baseline dataset so I can see which groups are underperforming?"}],"resultDescription":"Returns two independent analytical reports: (1) a secondary-measure weighted mean computed over the selected field (populated_features or expected_features) across the supplied rows, and (2) a keyed dataset comparison between the primary and secondary row arrays, highlighting differences by dataset_group and date. No external lookups or predictions are performed — all analysis uses local analytical engines on the supplied data only.","failureModes":["Exceeding 200 rows causes a validation error — keep each array at or under the 200-row limit","Missing required fields (row_id, dataset_group, date, populated_features, expected_features) on any row will cause a schema validation failure","Invalid date format (must match YYYY-MM-DD) will be rejected","Null values in populated_features or expected_features may affect weighted mean accuracy — check for missing data before submitting","Mismatched row_ids between primary and secondary arrays may reduce the quality of the keyed comparison report"],"whenToPreferThis":"Choose this endpoint when you need two simultaneous analytical reports — a weighted mean and a keyed dataset comparison — on feature availability data (populated vs expected features) in a single $0.01 call. It is ideal for data quality workflows, ML feature pipeline audits, and dataset completeness checks where you already have the data locally and need fast, offline statistical analysis without external lookups or predictions. Prefer this over general-purpose analytics tools when your data fits within 200 rows and you specifically need the weighted mean + keyed comparison combination.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:23.703Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-weighted-mean-keyed-dataset-comparison-6f21eaea"}