{"uid":"cap_2bmbceaXXylylgqjsbNAq","slug":"document-type-confidence-calibration-bins-1d853c1a","name":"Document Type Confidence Calibration Bins","description":"Document type - Confidence calibration bins: Confidence calibration bins for Document type. 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-document-type-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":["invoice","report","correspondence","other"],"type":"string"},"predicted":{"enum":["invoice","report","correspondence","other"],"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":"invoice sample document number 0 for this supplied dataset.","actual":"invoice","predicted":"invoice","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"invoice sample document number 1 for this supplied dataset.","actual":"invoice","predicted":"invoice","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"invoice sample document number 2 for this supplied dataset.","actual":"invoice","predicted":"invoice","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"report sample document number 0 for this supplied dataset.","actual":"report","predicted":"report","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"report sample document number 1 for this supplied dataset.","actual":"report","predicted":"report","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"report sample document number 2 for this supplied dataset.","actual":"report","predicted":"report","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"correspondence sample document number 0 for this supplied dataset.","actual":"correspondence","predicted":"correspondence","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"correspondence sample document number 1 for this supplied dataset.","actual":"correspondence","predicted":"correspondence","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"correspondence sample document number 2 for this supplied dataset.","actual":"correspondence","predicted":"correspondence","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-3-0","text":"other sample document number 0 for this supplied dataset.","actual":"other","predicted":"other","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-1","text":"other sample document number 1 for this supplied dataset.","actual":"other","predicted":"other","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-2","text":"other sample document number 2 for this supplied 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confidence calibration bins for a document-type classifier (invoice, report, correspondence, other) from up to 200 labelled prediction records with associated probabilities.","exampleAgentPrompt":"I have up to 200 labelled document records where each record has an id, the document text, the actual type (invoice, report, correspondence, or other), the predicted type, and a 4-element probability array — can you compute the confidence calibration bins to see how well my classifier's predicted probabilities match real accuracy?","exampleUseCases":[{"title":"Invoice classifier calibration audit","prompt":"I've been running a document classifier that flags invoices vs reports vs correspondence, and I want to know if its probability outputs are trustworthy — here are 150 labelled records with actual labels, predicted labels, and the four-class probability vectors. Can you compute the confidence calibration bins so I can see where the model is overconfident or underconfident?"},{"title":"Pre-deployment reliability check","prompt":"Before I ship this document-type model to production, I need to verify that when it says 80% confident it's an invoice, it's actually right about 80% of the time. I have 200 test records with ids, texts, actual classes, predicted classes, and probabilities — please generate the calibration bins for me."},{"title":"Comparing two model versions","prompt":"I retrained my document classifier and want to check if calibration improved. I have the predicted probabilities and ground-truth labels for 120 records from the new model — can you give me the calibration bin breakdown so I can compare it against the old model's bins?"}],"resultDescription":"A set of confidence calibration bins summarizing how well the classifier's predicted probability scores align with observed accuracy across binned probability ranges, including per-bin statistics useful for reliability diagrams and calibration error analysis.","failureModes":["Fewer than the minimum required records provided — returns error or degenerate bins","Probability arrays that do not sum to 1 or contain out-of-range values — validation error","Mismatch between number of classes and probability vector length (must be exactly 4) — schema error","Records exceeding 200 — truncation or rejection","Text fields exceeding 1000 characters — truncation or schema rejection","Empty records array — error response"],"whenToPreferThis":"Choose this endpoint when you need to evaluate the probability calibration of a document-type classifier operating over invoice, report, correspondence, and other categories, and you have up to 200 labelled prediction records with class probabilities. It is a lightweight, pay-per-call tool suited for spot evaluation and audit rather than large-scale training pipelines or deep learning retraining workflows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:52.603Z","isFirstParty":false,"canonicalSlug":"document-type-confidence-calibration-bins-1d853c1a"}