{"uid":"cap_xKdI5Tfly7Eg16HQpI5ET","slug":"return-reason-confidence-calibration-bins-452820fb","name":"Return Reason Confidence Calibration Bins","description":"Return reason - Confidence calibration bins: Confidence calibration bins for Return reason. 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-return-reason-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":["damaged","wrong_item","unwanted","other"],"type":"string"},"predicted":{"enum":["damaged","wrong_item","unwanted","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":"damaged sample document number 0 for this supplied dataset.","actual":"damaged","predicted":"damaged","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"damaged sample document number 1 for this supplied dataset.","actual":"damaged","predicted":"damaged","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"damaged sample document number 2 for this supplied dataset.","actual":"damaged","predicted":"damaged","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"wrong_item sample document number 0 for this supplied dataset.","actual":"wrong_item","predicted":"wrong_item","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"wrong_item sample document number 1 for this supplied dataset.","actual":"wrong_item","predicted":"wrong_item","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"wrong_item sample document number 2 for this supplied dataset.","actual":"wrong_item","predicted":"wrong_item","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"unwanted sample document number 0 for this supplied dataset.","actual":"unwanted","predicted":"unwanted","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"unwanted sample document number 1 for this supplied dataset.","actual":"unwanted","predicted":"unwanted","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"unwanted sample document number 2 for this supplied dataset.","actual":"unwanted","predicted":"unwanted","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 dataset.","actual":"other","predicted":"other","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_Az8v1xNIf6ljmwyA60JGm","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 return-reason classifier, given up to 200 labelled records with predicted probabilities and actual labels.","exampleAgentPrompt":"I have 150 labelled return records with actual labels (damaged, wrong_item, unwanted, other), predicted labels, and the 4-class probability arrays from my classifier — can you run confidence calibration binning on them so I can see how well the predicted probabilities match reality?","exampleUseCases":[{"title":"Evaluating e-commerce return classifier calibration","prompt":"I've got 120 customer return records where I know the real reason (damaged, wrong_item, unwanted, or other), plus what my classifier predicted and the 4 probability scores it output — can you compute the confidence calibration bins so I can see if the model is overconfident or underconfident?"},{"title":"Validating naive-Bayes model before production deploy","prompt":"Before I ship my return-reason model, I want to check its calibration. I have 200 labelled examples with actual and predicted return reasons and the probability arrays — please give me the calibration bins so I can decide if it needs isotonic regression or Platt scaling."},{"title":"Diagnosing miscalibration in a damaged-item detector","prompt":"My return classifier seems to be too confident when it predicts 'damaged' — can you run calibration bin analysis on my 80 test records with their actual labels, predicted labels, and probability vectors to show me where the probability estimates are off?"}],"resultDescription":"A confidence calibration bins deliverable: the supplied records are grouped into probability bins and the endpoint returns bin-level statistics showing how predicted confidence scores align with observed accuracy, enabling reliability diagram construction and calibration diagnosis for the four return-reason classes (damaged, wrong_item, unwanted, other).","failureModes":["Fewer than the minimum required records supplied — endpoint may return an error or degenerate bin output","Probability arrays not summing to 1 or containing values outside [0,1] — validation error","More than 200 records supplied — truncated or rejected input","Mismatched enum values in actual/predicted fields — schema validation failure","All records belonging to a single class — bins for minority classes may be empty or undefined"],"whenToPreferThis":"Use this endpoint when you need lightweight, pay-per-call confidence calibration bin computation for a return-reason classifier without standing up any infrastructure. It is purpose-built for the four return-reason classes (damaged, wrong_item, unwanted, other) and accepts up to 200 records with predicted probabilities, making it ideal for spot evaluation during model development or A/B testing. Prefer it over general-purpose ML platforms when you need a fast, cheap, bounded calibration check with no model hosting or training overhead.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T00:56:02.239Z","isFirstParty":false,"canonicalSlug":"return-reason-confidence-calibration-bins-452820fb"}