{"uid":"cap_k8V4DN17tSPRwQqoLZlam","slug":"ticket-priority-prediction-error-records-5c6e1e8c","name":"Ticket Priority Prediction Error Records","description":"Ticket priority - Prediction error records: Prediction error records 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 prediction error records deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-ticket-priority-errors?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"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_e2rxfQXoUDC0G5UxNpDh2","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 and returns prediction error records for a naive-Bayes ticket priority classifier given up to 200 labelled text records with actual and predicted labels plus class probabilities.","exampleAgentPrompt":"I have a set of 50 labelled support tickets with their actual priorities (low, normal, high), what my classifier predicted, and the class probabilities — can you run the prediction error records analysis on them so I can see exactly where the model went wrong?","exampleUseCases":[{"title":"Auditing support ticket triage model","prompt":"I've got 120 support tickets where my model predicted low, normal, or high priority — each one has the actual label and the three class probabilities. Can you send these through the ticket priority prediction error records tool so I can see which ones were misclassified?"},{"title":"Comparing naive-Bayes baseline accuracy","prompt":"I want to evaluate my naive-Bayes ticket priority classifier. I have 80 labelled records with actual and predicted labels plus probabilities for each class. Can you run the prediction error records analysis and tell me where the errors are?"},{"title":"Diagnosing high-priority ticket misses","prompt":"My ticket classifier keeps missing high-priority issues — I have 200 labelled examples with actual vs predicted priority and the probability scores. Can you compute the prediction error records so I can identify the patterns in where it fails?"}],"resultDescription":"Returns up to 200 prediction error records for the supplied ticket priority classification data, detailing per-record outcomes including misclassification flags and associated metadata derived from actual labels, predicted labels, and class probabilities for low, normal, and high priority classes.","failureModes":["Fewer than 2 records supplied returns a validation error","More than 200 records supplied exceeds the maxItems limit and is rejected","Probabilities array missing exactly 3 values causes schema validation failure","Probability values outside 0–1 range are rejected","Invalid enum values for actual or predicted fields (not low/normal/high) cause errors","Missing required fields (id, text, actual, predicted, probabilities) result in bad request errors"],"whenToPreferThis":"Choose this endpoint when you need per-record prediction error diagnostics for a ticket priority (low/normal/high) text classifier, especially when working with small labelled datasets up to 200 records and a naive-Bayes baseline. It is best suited for offline evaluation and auditing of classifier outputs rather than real-time inference or training large models.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T00:51:40.388Z","isFirstParty":false,"canonicalSlug":"ticket-priority-prediction-error-records-5c6e1e8c"}