{"uid":"cap_Nz54UZd3L8cGGi18a37jw","slug":"return-reason-prediction-error-records-93f18923","name":"Return Reason Prediction Error Records","description":"Return reason - Prediction error records: Prediction error records 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 prediction error records deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-errors?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_moYS33EDuVnb17rO3pASJ","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 prediction error records for a naive-Bayes return-reason classifier on up to 200 supplied labelled text records","exampleAgentPrompt":"I have up to 200 labelled return-reason records — each with an ID, the customer text, the actual class (damaged, wrong_item, unwanted, or other), the predicted class, and the four class probabilities — can you run them through the prediction error records tool so I can see exactly which records my model got wrong?","exampleUseCases":[{"title":"Diagnosing e-commerce return classifier mistakes","prompt":"I've got 150 labelled customer return requests where I know the true reason (damaged, wrong_item, unwanted, or other) and what my model predicted plus the four class probabilities for each — can you identify the prediction error records so I can see where the classifier is failing?"},{"title":"Validating naive-Bayes model before deployment","prompt":"Before I deploy my return-reason classifier, I want to check its errors on a held-out set of 80 records with IDs, texts, actual labels, predicted labels, and probabilities — can you pull back the prediction error records for that batch?"},{"title":"Auditing misclassifications for wrong-item returns","prompt":"My return reason model seems to be confusing 'wrong_item' and 'damaged' categories — I have 200 records with actual vs predicted labels and the probability vectors, can you compute the prediction error records so I can audit the specific misclassifications?"}],"resultDescription":"A bounded set of prediction error records for each supplied labelled text item, identifying where the predicted return-reason class differed from the actual class, along with supporting probability information per record — up to 200 records processed in one call.","failureModes":["Records array exceeds 200 items — request rejected","Missing required fields (id, text, actual, predicted, probabilities) on any record — validation error","Probabilities array does not contain exactly 4 values between 0 and 1 — schema error","Invalid enum value for actual or predicted (must be damaged, wrong_item, unwanted, or other) — rejection","Payment of 0.01 USDC not completed via x402 — 402 Payment Required"],"whenToPreferThis":"Choose this endpoint when you have a pre-labeled dataset for return-reason classification (with actual and predicted labels plus class probabilities) and need per-record error diagnostics from a lightweight naive-Bayes baseline evaluation — not a hosted model or foundation-model fine-tuning service. Ideal for small-scale offline evaluation, auditing classifier output, or rapid error analysis without spinning up your own ML infrastructure.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T19:16:39.367Z","isFirstParty":false,"canonicalSlug":"return-reason-prediction-error-records-93f18923"}