{"uid":"cap_E51GCII-UlsYoFPU32hFe","slug":"return-reason-label-and-prediction-audit-f7982dec","name":"Return Reason Label and Prediction Audit","description":"Return reason - Label and prediction audit: Label and prediction audit 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 label and prediction audit deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-label-audit?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_dcSrBzSpW13ZHMO4hVzDs","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":"Audits up to 200 labelled text records for return-reason classification accuracy, computing evaluation metrics against actual vs. predicted labels across four categories.","exampleAgentPrompt":"Can you audit my return reason classifier on these 50 labelled records — each has the customer text, the actual reason (damaged, wrong_item, unwanted, or other), the predicted label, and the four class probabilities — and tell me where the model is going wrong?","exampleUseCases":[{"title":"Post-deployment model accuracy check","prompt":"I just deployed a naive-Bayes return reason classifier and I have 150 labelled records with actual labels, predicted labels, and probabilities for damaged, wrong_item, unwanted, and other — can you run a full prediction audit and show me the accuracy breakdown?"},{"title":"Label quality audit before retraining","prompt":"Before I retrain my return reason model, I want to audit 200 of my labelled records to see how consistent the actual labels are compared to what the model predicted — here are the texts, actual reasons, predicted reasons, and class probabilities."},{"title":"Spot-checking misclassified returns","prompt":"My return reason model keeps confusing damaged with wrong_item — can you audit these 80 records with their actual and predicted labels plus probabilities so I can see exactly where it's going wrong?"}],"resultDescription":"A bounded audit report covering the supplied labelled records, including accuracy metrics, per-class performance breakdown, comparison of actual vs. predicted return reason labels (damaged, wrong_item, unwanted, other), and identification of misclassified records based on the supplied class probabilities.","failureModes":["More than 200 records supplied — batch is capped at 200","Missing or malformed probability arrays (must have exactly 4 values summing to ~1.0)","Invalid actual or predicted label values outside the four allowed enum values","Records with text exceeding 1000 characters or IDs exceeding 80 characters","Empty records array returns no meaningful audit output"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, low-cost ($0.01) audit of a return-reason text classifier on up to 200 pre-labelled records. Ideal for spot-checking model quality, validating label consistency, or evaluating naive-Bayes baselines without access to a hosted ML platform. Best suited for e-commerce or logistics teams tracking return categorization accuracy across the four standard classes: damaged, wrong_item, unwanted, and other.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:48:34.957Z","isFirstParty":false,"canonicalSlug":"return-reason-label-and-prediction-audit-f7982dec"}