{"uid":"cap_Q4tW4mNguWJSBNho05Q3y","slug":"training-question-top-k-label-coverage-1a2fbcc0","name":"Training Question Top-k Label Coverage","description":"Training question - Top-k label coverage: Top-k label coverage for Training question. 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-training-question-top-k?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"k":{"type":"integer","default":2,"maximum":4,"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":["concept","procedure","example","assessment"],"type":"string"},"predicted":{"enum":["concept","procedure","example","assessment"],"type":"string"},"probabilities":{"type":"array","items":{"type":"number","default":0,"maximum":1,"minimum":0},"default":[],"maxItems":4,"minItems":4}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"concept sample document number 0 for this supplied dataset.","actual":"concept","predicted":"concept","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"concept sample document number 1 for this supplied dataset.","actual":"concept","predicted":"concept","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"concept sample document number 2 for this supplied dataset.","actual":"concept","predicted":"concept","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"procedure sample document number 0 for this supplied dataset.","actual":"procedure","predicted":"procedure","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"procedure sample document number 1 for this supplied dataset.","actual":"procedure","predicted":"procedure","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"procedure sample document number 2 for this supplied dataset.","actual":"procedure","predicted":"procedure","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"example sample document number 0 for this supplied dataset.","actual":"example","predicted":"example","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"example sample document number 1 for this supplied dataset.","actual":"example","predicted":"example","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"example sample document number 2 for this supplied dataset.","actual":"example","predicted":"example","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-3-0","text":"assessment sample document number 0 for this supplied dataset.","actual":"assessment","predicted":"assessment","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-1","text":"assessment sample document number 1 for this supplied dataset.","actual":"assessment","predicted":"assessment","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-2","text":"assessment sample document number 2 for this supplied dataset.","actual":"assessment","predicted":"assessment","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]}],"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_3RqrTmkOMlvB-KNOP9NCJ","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 set of labelled training question text records with classifier probability outputs","exampleAgentPrompt":"Can you compute the top-2 label coverage for my training question classifier? I have 12 labeled records with actual labels (concept, procedure, example, or assessment), predicted labels, and probability arrays for each — here's the data.","exampleUseCases":[{"title":"Evaluating educational content classifier","prompt":"I've got 50 labeled training question records — each tagged as concept, procedure, example, or assessment — along with my model's predicted label and 4-class probability scores. Can you calculate the top-3 label coverage to see how often the true label falls within the top 3 predictions?"},{"title":"Comparing top-k thresholds for QA pipeline","prompt":"I'm tuning a quiz question classifier and want to know the top-2 label coverage across my 30 labeled samples. Each record has actual and predicted labels plus a 4-value probability array. Can you run the top-k coverage calculation with k=2?"},{"title":"Benchmarking naive-Bayes baseline model","prompt":"We built a naive-Bayes baseline for classifying training questions into concept, procedure, example, and assessment. I have 80 labeled records with their probability outputs — can you compute the top-1 label coverage to see how accurate our baseline is?"}],"resultDescription":"A bounded top-k label coverage deliverable: a computed coverage score indicating the fraction of records where the true label appears among the top k highest-probability predictions, given the supplied labelled text records and their classifier probability outputs.","failureModes":["Invalid k value outside range 1-4 returns error","Records array exceeding 200 items rejected","Probability arrays not summing correctly or containing values outside 0-1 may cause errors","Invalid label values outside the four allowed enums (concept, procedure, example, assessment) return validation errors","Empty records array returns error or trivial result","Payment failure via x402 protocol prevents response"],"whenToPreferThis":"Choose this endpoint when you need a fast, inexpensive top-k label coverage metric for a small batch of up to 200 labelled text records in the training question domain with a fixed four-class label set (concept, procedure, example, assessment). Ideal for evaluating naive-Bayes or similar baseline classifiers on educational content without needing a hosted model or foundation model infrastructure.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:43:10.717Z","isFirstParty":false,"canonicalSlug":"training-question-top-k-label-coverage-1a2fbcc0"}