{"uid":"cap_K3VtSNjIln3UlFxccdI1U","slug":"training-question-confidence-calibration-bins-d99037b2","name":"Training Question Confidence Calibration Bins","description":"Training question - Confidence calibration bins: Confidence calibration bins 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 confidence calibration bins deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-training-question-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":["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_aqCOarr68470QVinNsKKp","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 text classifier that categorizes training questions into concept, procedure, example, or assessment classes.","exampleAgentPrompt":"Can you compute confidence calibration bins for my training question classifier? I have 50 labeled records where each has the actual label (concept, procedure, example, or assessment), the predicted label, and a 4-element probability vector — I want to see how well my model's confidence scores align with real accuracy.","exampleUseCases":[{"title":"Diagnosing overconfident classifier on course content","prompt":"My text classifier for categorizing training questions seems overconfident — it says 90% probability but is wrong a lot. Can you run confidence calibration bins on these 80 labeled records (each with actual and predicted labels of concept, procedure, example, or assessment, plus a 4-element probability array) so I can see how badly miscalibrated it is?"},{"title":"Validating naive Bayes model before deployment","prompt":"Before I ship this naive Bayes classifier for tagging training questions, I want to check its calibration. I have 120 records with actual labels, predicted labels, and probability vectors across concept, procedure, example, and assessment — can you generate the calibration bin breakdown so I can decide if I need to apply Platt scaling?"},{"title":"Comparing calibration across two classifier versions","prompt":"I'm comparing two versions of my training question classifier and want calibration bin stats for version B. Here are 60 records with the actual class, predicted class, and 4-value probability distribution for each — please give me the confidence calibration bins so I can compare reliability to version A."}],"resultDescription":"Returns confidence calibration bins showing how the classifier's predicted probabilities map to observed accuracy — typically bucketed probability ranges with the mean predicted confidence and actual accuracy per bin, enabling identification of over- or under-confidence across the concept, procedure, example, and assessment label classes.","failureModes":["Fewer than the minimum required records provided — may return empty or degenerate bins","Probability vectors that don't sum to 1 or contain values outside [0,1] — validation error","Records with missing required fields (id, text, actual, predicted, probabilities) — schema rejection","More than 200 records supplied — truncation or rejection based on limit","Mismatch between actual/predicted enum values and allowed classes — error"],"whenToPreferThis":"Use this endpoint when you need lightweight, local confidence calibration analysis on up to 200 labeled records for a 4-class training question classifier (concept, procedure, example, assessment) without hosting a model or calling a foundation model API. Prefer it over general ML evaluation platforms when you want a quick, cheap ($0.01 USDC) calibration bin report on small datasets, especially in agentic pipelines that use x402 micropayment flows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T06:52:52.854Z","isFirstParty":false,"canonicalSlug":"training-question-confidence-calibration-bins-d99037b2"}