{"uid":"cap_GtoXfbHogB_yA-il2aNpZ","slug":"knowledge-topic-classifier-confidence-calibration-bins-adbbc7c5","name":"Knowledge Topic Classifier – Confidence Calibration Bins","description":"Knowledge topic - Confidence calibration bins: Confidence calibration bins for Knowledge topic. 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 confidence calibration bins deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-calibration?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":["setup","usage","troubleshooting","reference"],"type":"string"},"predicted":{"enum":["setup","usage","troubleshooting","reference"],"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":"setup sample document number 0 for this supplied dataset.","actual":"setup","predicted":"setup","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"setup sample document number 1 for this supplied dataset.","actual":"setup","predicted":"setup","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"setup sample document number 2 for this supplied dataset.","actual":"setup","predicted":"setup","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"usage sample document number 0 for this supplied dataset.","actual":"usage","predicted":"usage","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"usage sample document number 1 for this supplied dataset.","actual":"usage","predicted":"usage","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"usage sample document number 2 for this supplied dataset.","actual":"usage","predicted":"usage","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"troubleshooting sample document number 0 for this supplied dataset.","actual":"troubleshooting","predicted":"troubleshooting","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"troubleshooting sample document number 1 for this supplied dataset.","actual":"troubleshooting","predicted":"troubleshooting","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"troubleshooting sample document number 2 for this supplied dataset.","actual":"troubleshooting","predicted":"troubleshooting","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-3-0","text":"reference sample document number 0 for this supplied dataset.","actual":"reference","predicted":"reference","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-1","text":"reference sample document number 1 for this supplied dataset.","actual":"reference","predicted":"reference","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-2","text":"reference sample document number 2 for this supplied dataset.","actual":"reference","predicted":"reference","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_tM2tkV9GXywm1YdiBFmuF","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 confidence calibration bins for a naive-Bayes knowledge-topic classifier across up to 200 labelled text records with actual labels, predicted labels, and class probabilities.","exampleAgentPrompt":"I have up to 200 labelled knowledge-topic records — each with the document text, the actual category (setup, usage, troubleshooting, or reference), the predicted category, and the four class probabilities — can you run confidence calibration bins on them so I can see how well my model's confidence scores align with its actual accuracy?","exampleUseCases":[{"title":"Auditing classifier overconfidence before deployment","prompt":"Before we ship our knowledge-base topic classifier, I want to check whether its confidence scores are actually calibrated — here are 150 labelled records with actual and predicted categories plus their four class probabilities. Can you produce the confidence calibration bins so I can see where the model is overconfident?"},{"title":"Comparing two model versions' calibration","prompt":"I've got predictions from two rounds of my naive-Bayes knowledge topic model and I want to compare their calibration quality. Start with this first batch of 120 records — actual labels are setup/usage/troubleshooting/reference, I have the predicted labels and probabilities for each. Give me the calibration bins so I can benchmark the baseline."},{"title":"Diagnosing poor reliability on troubleshooting class","prompt":"My support-article classifier seems to be poorly calibrated specifically on the troubleshooting category. I have 80 labelled examples with actual and predicted labels and probability vectors — can you run the confidence calibration bins analysis so I can pinpoint where the predicted probabilities are diverging from real accuracy?"}],"resultDescription":"Returns a set of confidence calibration bins summarizing how well the classifier's predicted probabilities align with observed accuracy across the four knowledge-topic classes (setup, usage, troubleshooting, reference). Each bin groups records by predicted confidence level and reports the mean confidence and actual accuracy within that bin, enabling reliability-diagram analysis and detection of over- or under-confidence.","failureModes":["Too many records supplied (over 200 items) — request rejected or truncated","Probability vectors not summing to 1 or containing values outside [0,1] — validation error","Mismatched enum values in actual/predicted fields (values outside setup/usage/troubleshooting/reference) — schema validation failure","Fewer than 4 probability values per record (minItems:4 not met) — schema error","Empty records array — returns empty or degenerate calibration output","Payment not processed (x402 protocol failure) — 402 response with no result"],"whenToPreferThis":"Choose this endpoint when you need calibration bin metrics specifically for a four-class knowledge-topic classifier (setup, usage, troubleshooting, reference) and have up to 200 labelled records with class probability vectors available. It is ideal for post-training evaluation of naive-Bayes or similar local models where you want to assess probability reliability without sending data to a hosted foundation model. Prefer it over generic calibration libraries when you need a pay-per-call, infrastructure-free evaluation deliverable in an agentic workflow.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-14T00:37:39.378Z","isFirstParty":false,"canonicalSlug":"knowledge-topic-classifier-confidence-calibration-bins-adbbc7c5"}