{"uid":"cap_HQPUshljIPcO6LUOKN87o","slug":"return-reason-classifier-per-label-precision-recall-79e9855e","name":"Return Reason Classifier — Per-Label Precision & Recall","description":"Return reason - Per-label precision and recall: Per-label precision and recall 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 per-label precision and recall deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-per-label?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"records":{"type":"array","items":{"type":"object","required":["id","text","actual","predicted"],"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"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"damaged sample document number 0 for this supplied dataset.","actual":"damaged","predicted":"damaged"},{"id":"example-0-1","text":"damaged sample document number 1 for this supplied dataset.","actual":"damaged","predicted":"damaged"},{"id":"example-0-2","text":"damaged sample document number 2 for this supplied dataset.","actual":"damaged","predicted":"damaged"},{"id":"example-1-0","text":"wrong_item sample document number 0 for this supplied dataset.","actual":"wrong_item","predicted":"wrong_item"},{"id":"example-1-1","text":"wrong_item sample document number 1 for this supplied dataset.","actual":"wrong_item","predicted":"wrong_item"},{"id":"example-1-2","text":"wrong_item sample document number 2 for this supplied dataset.","actual":"wrong_item","predicted":"wrong_item"},{"id":"example-2-0","text":"unwanted sample document number 0 for this supplied dataset.","actual":"unwanted","predicted":"unwanted"},{"id":"example-2-1","text":"unwanted sample document number 1 for this supplied dataset.","actual":"unwanted","predicted":"unwanted"},{"id":"example-2-2","text":"unwanted sample document number 2 for this supplied dataset.","actual":"unwanted","predicted":"unwanted"},{"id":"example-3-0","text":"other sample document number 0 for this supplied dataset.","actual":"other","predicted":"other"},{"id":"example-3-1","text":"other sample document number 1 for this supplied dataset.","actual":"other","predicted":"other"},{"id":"example-3-2","text":"other sample document number 2 for this supplied dataset.","actual":"other","predicted":"other"}],"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_jKkESvOFEbvsIBwvHrxGy","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 per-label precision and recall metrics for a return-reason text classifier across up to 200 labelled records with actual and predicted labels.","exampleAgentPrompt":"I have 150 labelled return reason records — each with the customer text, the actual label (damaged, wrong_item, unwanted, or other), and what my classifier predicted. Can you compute the per-label precision and recall so I can see which categories my model gets right and which it confuses?","exampleUseCases":[{"title":"Diagnose weak label in return classifier","prompt":"My return-reason classifier seems to mix up 'unwanted' and 'wrong_item' returns. I've got 120 labelled examples with both the actual and predicted labels — can you compute precision and recall for each label so I can see exactly where it's going wrong?"},{"title":"Benchmark naive Bayes return model","prompt":"I just trained a simple naive Bayes model on return reason data and I want to know how it performs per category. Here are 80 records with the text, the actual return reason (damaged, wrong_item, unwanted, or other), and the model's predictions — give me the precision and recall for each label."},{"title":"Pre-launch model quality check","prompt":"Before we go live with our automated returns triage, I need to verify the classifier's per-label precision and recall on a held-out set. I have 200 records with actual and predicted return reason labels — can you run the evaluation and tell me which labels are underperforming?"}],"resultDescription":"A per-label breakdown of precision and recall for each return reason category (damaged, wrong_item, unwanted, other), computed from the supplied actual-vs-predicted label pairs across up to 200 records.","failureModes":["More than 200 records submitted — endpoint enforces an upper limit","Missing required fields (id, text, actual, predicted) on any record — validation error","Invalid label values outside the enum (damaged, wrong_item, unwanted, other) — schema rejection","Empty records array — no metrics can be computed","Payment not completed via x402 — request blocked"],"whenToPreferThis":"Choose this endpoint when you need a quick, cheap ($0.01 USDC) per-class precision and recall evaluation for a return-reason text classifier without setting up local tooling. It is ideal for spot-checking model quality on small labelled datasets (up to 200 records) across the four standard return reason categories. Prefer alternatives if you need macro/micro aggregated F1, confusion matrices, or support for custom label sets beyond the four built-in categories.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T00:50:29.476Z","isFirstParty":false,"canonicalSlug":"return-reason-classifier-per-label-precision-recall-79e9855e"}