{"uid":"cap_5kV-qLi8jjG3HPU4BR_Mt","slug":"ticket-priority-classifier-per-label-precision-and-recall-8d17c4f9","name":"Ticket Priority Classifier — Per-Label Precision and Recall","description":"Ticket priority - Per-label precision and recall: Per-label precision and recall for Ticket priority. 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-ticket-priority-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":["low","normal","high"],"type":"string"},"predicted":{"enum":["low","normal","high"],"type":"string"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"low sample document number 0 for this supplied dataset.","actual":"low","predicted":"low"},{"id":"example-0-1","text":"low sample document number 1 for this supplied dataset.","actual":"low","predicted":"low"},{"id":"example-0-2","text":"low sample document number 2 for this supplied dataset.","actual":"low","predicted":"low"},{"id":"example-1-0","text":"normal sample document number 0 for this supplied dataset.","actual":"normal","predicted":"normal"},{"id":"example-1-1","text":"normal sample document number 1 for this supplied dataset.","actual":"normal","predicted":"normal"},{"id":"example-1-2","text":"normal sample document number 2 for this supplied dataset.","actual":"normal","predicted":"normal"},{"id":"example-2-0","text":"high sample document number 0 for this supplied dataset.","actual":"high","predicted":"high"},{"id":"example-2-1","text":"high sample document number 1 for this supplied dataset.","actual":"high","predicted":"high"},{"id":"example-2-2","text":"high sample document number 2 for this supplied dataset.","actual":"high","predicted":"high"}],"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_ofngGLNesfl_IRe4ZwgRU","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 ticket priority classifier across low, normal, and high priority classes using up to 200 labeled text records.","exampleAgentPrompt":"Can you evaluate my ticket priority classifier by computing per-label precision and recall? I have 150 support ticket records each with the ticket text, actual priority label (low, normal, or high), and my model's predicted label — I want to see how well it performs on each priority class separately.","exampleUseCases":[{"title":"Support team classifier audit","prompt":"I have 120 labeled support tickets where each ticket has the text, the true priority (low, normal, or high), and what my classifier predicted. Can you calculate the precision and recall for each priority level so I can see where my model is making mistakes?"},{"title":"High-priority detection validation","prompt":"My ticket triage model might be missing critical high-priority issues. I have 80 labeled tickets with actual and predicted priorities — can you give me the per-label precision and recall breakdown so I can specifically check how accurate it is on the high-priority class?"},{"title":"Model iteration benchmarking","prompt":"I just retrained my ticket priority classifier and want to compare it to before. Here are 200 labeled records with the ticket text, ground truth priority labels, and new model predictions — please compute the per-label precision and recall for low, normal, and high so I can see if it improved."}],"resultDescription":"Returns per-label precision and recall scores for each of the three ticket priority classes (low, normal, high), computed from up to 200 supplied records containing ticket text, actual labels, and predicted labels. Useful for diagnosing class-specific model weaknesses.","failureModes":["Fewer than 2 records supplied — minimum record count not met","Records missing required fields (id, text, actual, predicted)","Label values outside allowed enum (low, normal, high)","More than 200 records submitted — exceeds maximum batch size","Record ID exceeds 80 character limit or is empty","Text field exceeds 1000 character limit per record","Payment not completed — x402 payment required at $0.01 USDC"],"whenToPreferThis":"Choose this endpoint when you need a granular, per-class evaluation of a ticket priority classifier — specifically when you want to distinguish precision and recall independently for each of the low, normal, and high priority labels rather than just an overall accuracy or macro average. Ideal for diagnosing which priority class a model under-performs on, validating classifier changes, or auditing triage automation quality. Best suited for small to medium evaluation sets (up to 200 records) where you supply your own ground truth and predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T19:13:47.259Z","isFirstParty":false,"canonicalSlug":"ticket-priority-classifier-per-label-precision-and-recall-8d17c4f9"}