{"uid":"cap_AaVMnqNnnGqAdcY6l-ysS","slug":"ticket-priority-top-k-label-coverage-c13e8408","name":"Ticket Priority Top-k Label Coverage","description":"Ticket priority - Top-k label coverage: Top-k label coverage 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 top-k label coverage deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-ticket-priority-top-k?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"k":{"type":"integer","default":2,"maximum":3,"minimum":1},"records":{"type":"array","items":{"type":"object","required":["id","text","actual","predicted","probabilities"],"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"},"probabilities":{"type":"array","items":{"type":"number","default":0,"maximum":1,"minimum":0},"default":[],"maxItems":3,"minItems":3}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"low sample document number 0 for this supplied dataset.","actual":"low","predicted":"low","probabilities":[0.8,0.1,0.1]},{"id":"example-0-1","text":"low sample document number 1 for this supplied dataset.","actual":"low","predicted":"low","probabilities":[0.8,0.1,0.1]},{"id":"example-0-2","text":"low sample document number 2 for this supplied dataset.","actual":"low","predicted":"low","probabilities":[0.8,0.1,0.1]},{"id":"example-1-0","text":"normal sample document number 0 for this supplied dataset.","actual":"normal","predicted":"normal","probabilities":[0.1,0.8,0.1]},{"id":"example-1-1","text":"normal sample document number 1 for this supplied dataset.","actual":"normal","predicted":"normal","probabilities":[0.1,0.8,0.1]},{"id":"example-1-2","text":"normal sample document number 2 for this supplied dataset.","actual":"normal","predicted":"normal","probabilities":[0.1,0.8,0.1]},{"id":"example-2-0","text":"high sample document number 0 for this supplied dataset.","actual":"high","predicted":"high","probabilities":[0.1,0.1,0.8]},{"id":"example-2-1","text":"high sample document number 1 for this supplied dataset.","actual":"high","predicted":"high","probabilities":[0.1,0.1,0.8]},{"id":"example-2-2","text":"high sample document number 2 for this supplied dataset.","actual":"high","predicted":"high","probabilities":[0.1,0.1,0.8]}],"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_MAE7i-dyuba1oqn8FJdfy","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 top-k label coverage metric for a ticket priority classifier (low/normal/high) given up to 200 labelled records with predicted probabilities.","exampleAgentPrompt":"Calculate the top-2 label coverage for my ticket priority classifier — I have 50 labelled support tickets with their actual priorities (low, normal, or high), predicted labels, and the three class probabilities for each. Can you run the coverage evaluation?","exampleUseCases":[{"title":"Evaluate ticket classifier before deployment","prompt":"I'm about to deploy a ticket priority model and want to know the top-2 label coverage on my 120 test records — each has the actual priority (low, normal, or high), the predicted label, and the three class probabilities. Can you compute the coverage score so I know how reliably the correct label appears in the model's top-2 predictions?"},{"title":"Compare naive-Bayes model top-k performance","prompt":"I trained a naive-Bayes ticket priority classifier and have 80 labelled records with actual labels, predictions, and probability scores. Run a top-1 label coverage check so I can see if the model is getting the right priority label as its top prediction."},{"title":"QA check on support ticket routing model","prompt":"We're doing a quality audit on our ticket routing model — I have 200 records with ground-truth priority labels (low, normal, high), the model's predicted labels, and the 3-class probability distributions. Can you compute the top-3 label coverage to see how often the correct priority appears somewhere in the model's top 3?"}],"resultDescription":"A bounded top-k label coverage deliverable: a metric indicating what fraction of supplied labelled records have the actual (true) label appearing within the top-k most probable predicted classes, based on the provided probability distributions for the low/normal/high ticket priority classes.","failureModes":["More than 200 records submitted — exceeds maximum batch size","Probabilities array does not contain exactly 3 values per record — schema violation","k value outside 1–3 range — rejected by schema","Actual or predicted label not in enum (low, normal, high) — validation error","Empty records array — no data to evaluate","Probability values outside 0–1 range — schema validation failure"],"whenToPreferThis":"Choose this endpoint when you need a quick, low-cost evaluation of top-k label coverage specifically for a ticket priority classifier with three classes (low, normal, high) and you already have model predictions and probabilities. It is ideal for offline model evaluation, QA checks, and small dataset analysis without needing a hosted model or foundation-model infrastructure. Prefer alternatives if you need to train a model, run inference on unlabelled data, or evaluate a classifier with different label sets.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T00:55:33.911Z","isFirstParty":false,"canonicalSlug":"ticket-priority-top-k-label-coverage-c13e8408"}