{"uid":"cap_2GcPIszszqZ2M0nBKiwgI","slug":"release-change-classifier-per-label-precision-and-recall-50434638","name":"Release Change Classifier — Per-Label Precision and Recall","description":"Release change - Per-label precision and recall: Per-label precision and recall for Release change. Up to 200 supplied labelled text records. Local dataset tools, evaluation and small naive-Bayes baselines; no hosted model or foundation-model training. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded per-label precision and recall deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-release-change-per-label?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"records":{"type":"array","items":{"type":"object","required":["id","text","actual","predicted"],"properties":{"id":{"type":"string","default":"","maxLength":80,"minLength":1},"text":{"type":"string","default":"","maxLength":1000},"actual":{"enum":["feature","fix","documentation","maintenance"],"type":"string"},"predicted":{"enum":["feature","fix","documentation","maintenance"],"type":"string"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"feature sample document number 0 for this supplied dataset.","actual":"feature","predicted":"feature"},{"id":"example-0-1","text":"feature sample document number 1 for this supplied dataset.","actual":"feature","predicted":"feature"},{"id":"example-0-2","text":"feature sample document number 2 for this supplied dataset.","actual":"feature","predicted":"feature"},{"id":"example-1-0","text":"fix sample document number 0 for this supplied dataset.","actual":"fix","predicted":"fix"},{"id":"example-1-1","text":"fix sample document number 1 for this supplied dataset.","actual":"fix","predicted":"fix"},{"id":"example-1-2","text":"fix sample document number 2 for this supplied dataset.","actual":"fix","predicted":"fix"},{"id":"example-2-0","text":"documentation sample document number 0 for this supplied dataset.","actual":"documentation","predicted":"documentation"},{"id":"example-2-1","text":"documentation sample document number 1 for this supplied dataset.","actual":"documentation","predicted":"documentation"},{"id":"example-2-2","text":"documentation sample document number 2 for this supplied dataset.","actual":"documentation","predicted":"documentation"},{"id":"example-3-0","text":"maintenance sample document number 0 for this supplied dataset.","actual":"maintenance","predicted":"maintenance"},{"id":"example-3-1","text":"maintenance sample document number 1 for this supplied dataset.","actual":"maintenance","predicted":"maintenance"},{"id":"example-3-2","text":"maintenance sample document number 2 for this supplied dataset.","actual":"maintenance","predicted":"maintenance"}],"maxItems":200,"minItems":2}}},"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_p2HJBu-_rZIAvGZnQi1O4","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 per-label precision and recall metrics for a naive-Bayes release-change classifier across up to 200 labelled text records with four categories: feature, fix, documentation, maintenance.","exampleAgentPrompt":"I have 120 labelled release notes where each record has an actual label and a predicted label — categories are feature, fix, documentation, and maintenance. Can you compute the per-label precision and recall so I can see which categories my classifier struggles with?","exampleUseCases":[{"title":"Auditing a release-note tagger","prompt":"I trained a classifier on changelog entries that tags each one as feature, fix, documentation, or maintenance. I've got 80 records with both actual and predicted labels — can you compute per-label precision and recall so I know which categories it's getting wrong?"},{"title":"Comparing two classifier versions","prompt":"I have 150 labelled release notes from our last sprint and I want to see per-label precision and recall broken down by feature, fix, documentation, and maintenance — this is for the v2 model so I can compare against v1 results."},{"title":"Detecting recall gaps in maintenance tagging","prompt":"Our release classifier seems to miss maintenance items a lot. Can you run per-label precision and recall on these 60 labelled records — actual vs predicted across feature, fix, documentation, and maintenance — so I can confirm whether recall for maintenance is the real problem?"}],"resultDescription":"Returns precision and recall values for each of the four labels (feature, fix, documentation, maintenance), computed from the supplied records' actual vs. predicted label pairs — giving a diagnostic breakdown of classifier performance at the class level.","failureModes":["More than 200 records supplied — exceeds the bounded deliverable limit","Missing required fields (id, text, actual, or predicted) in any record","Invalid label value outside the allowed enum (feature, fix, documentation, maintenance)","Empty records array — no data to evaluate","Payment failure via x402 protocol"],"whenToPreferThis":"Choose this endpoint when you need fast, per-label precision and recall metrics for a four-class release-change text classifier without standing up your own evaluation pipeline. It is ideal for lightweight, one-off evaluations of up to 200 records and when the label set is exactly: feature, fix, documentation, maintenance. Use it over general ML platforms when cost, setup time, and simplicity are priorities.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:47:40.793Z","isFirstParty":false,"canonicalSlug":"release-change-classifier-per-label-precision-and-recall-50434638"}