{"uid":"cap_HW7vfse5MpM9LqXNDFHPc","slug":"ticket-priority-fixed-holdout-learning-curve-61f4a0b3","name":"Ticket Priority Fixed-Holdout Learning Curve","description":"Ticket priority - Fixed-holdout learning curve: Fixed-holdout learning curve 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 fixed-holdout learning curve deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-ticket-priority-learning-curve?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"records":{"type":"array","items":{"type":"object","required":["id","text","actual"],"properties":{"id":{"type":"string","default":"","maxLength":80,"minLength":1},"text":{"type":"string","default":"","maxLength":1000},"actual":{"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"},{"id":"example-0-1","text":"low sample document number 1 for this supplied dataset.","actual":"low"},{"id":"example-0-2","text":"low sample document number 2 for this supplied dataset.","actual":"low"},{"id":"example-1-0","text":"normal sample document number 0 for this supplied dataset.","actual":"normal"},{"id":"example-1-1","text":"normal sample document number 1 for this supplied dataset.","actual":"normal"},{"id":"example-1-2","text":"normal sample document number 2 for this supplied dataset.","actual":"normal"},{"id":"example-2-0","text":"high sample document number 0 for this supplied dataset.","actual":"high"},{"id":"example-2-1","text":"high sample document number 1 for this supplied dataset.","actual":"high"},{"id":"example-2-2","text":"high sample document number 2 for this supplied dataset.","actual":"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_cQul3AdipWwDh3y7Zb3aM","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":"Generates a fixed-holdout learning curve for a ticket priority classifier (low/normal/high) using up to 200 labelled text records and a naive-Bayes baseline.","exampleAgentPrompt":"Run a fixed-holdout learning curve for my ticket priority classifier using these labelled support tickets — each ticket has an ID, the ticket text, and its actual priority (low, normal, or high) — I've got about 120 records and want to see how classification performance changes as training size grows.","exampleUseCases":[{"title":"Assess data sufficiency for ticket triage","prompt":"I have 150 labelled support tickets tagged as low, normal, or high priority — can you run a fixed-holdout learning curve so I can see whether adding more labelled examples would meaningfully improve my ticket classifier?"},{"title":"Benchmark naive-Bayes on priority labels","prompt":"Run a fixed-holdout learning curve benchmark on my 80 labelled ticket records (each with an ID, text, and a low/normal/high priority label) so I can see the naive-Bayes baseline accuracy at different training sizes."},{"title":"Validate classifier before production rollout","prompt":"Before we deploy our ticket priority model, can you evaluate it with a fixed holdout split across these 200 labelled tickets and give me a learning curve showing accuracy vs training data size for the low, normal, and high classes?"}],"resultDescription":"Returns a fixed-holdout learning curve deliverable showing how classification performance (accuracy or related metrics) changes as the number of training examples increases, computed using a naive-Bayes baseline on the supplied labelled ticket text records split with a fixed holdout. Includes evaluation metrics across training-size increments.","failureModes":["Fewer than 2 records supplied — minimum input not met","Records missing required fields (id, text, actual) — validation error","More than 200 records supplied — exceeds maxItems limit","Invalid priority label (not low/normal/high) — enum validation failure","Text field exceeds 1000 characters per record — truncation or rejection","Insufficient class representation to form a meaningful holdout split"],"whenToPreferThis":"Choose this endpoint when you have up to 200 labelled support ticket texts with low/normal/high priority labels and want to understand how classifier performance scales with training data size using a lightweight naive-Bayes model — no hosted model or foundation model required. Ideal for early-stage data assessment, data sufficiency analysis, and local benchmarking before committing to larger ML pipelines.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T00:54:07.513Z","isFirstParty":false,"canonicalSlug":"ticket-priority-fixed-holdout-learning-curve-61f4a0b3"}