{"uid":"cap_x6lYKHqbzklUNkR1Cwt--","slug":"return-reason-classifier-macro-micro-weighted-aggregate-scores-1312aa0e","name":"Return Reason Classifier — Macro, Micro & Weighted Aggregate Scores","description":"Return reason - Macro micro and weighted scores: Macro micro and weighted scores 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 macro micro and weighted scores deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-aggregate-scores?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_q1Eb-V230R3UinD-0u18y","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 macro, micro, and weighted precision/recall/F1 aggregate scores for a return-reason text classification dataset with up to 200 labelled records.","exampleAgentPrompt":"Can you evaluate my return reason classifier? I have up to 200 labelled records where each record has an ID, the customer review text, the actual return category (damaged, wrong_item, unwanted, or other), and my model's predicted category — give me the macro, micro, and weighted precision/recall/F1 scores.","exampleUseCases":[{"title":"Benchmarking a new returns classifier","prompt":"I just trained a naive Bayes model to classify customer return reasons into damaged, wrong_item, unwanted, or other. I have 150 labelled test records with IDs, texts, actual labels, and my model's predictions — can you compute the macro, micro, and weighted F1 scores so I can see how well it's doing?"},{"title":"Comparing two model versions","prompt":"I have predictions from two versions of my return reason model on the same 120 labelled records. Can you score version A for me first — give me macro, micro, and weighted precision, recall, and F1 — so I have a baseline to compare against?"},{"title":"Auditing class imbalance impact","prompt":"Our return dataset is heavily skewed toward 'unwanted' returns. Can you run the macro, micro, and weighted aggregate scoring on my 80 labelled records so I can see how much the class imbalance is hurting my model's macro F1 versus its weighted F1?"}],"resultDescription":"Returns macro, micro, and weighted aggregate classification metrics (precision, recall, F1) computed across the four return reason classes (damaged, wrong_item, unwanted, other) for the supplied labelled dataset of up to 200 records.","failureModes":["More than 200 records supplied — request rejected or truncated","Records missing required fields (id, text, actual, predicted) — validation error","Invalid enum values in actual or predicted fields — schema rejection","Empty records array — no metrics can be computed","Malformed JSON payload — HTTP 400 bad request"],"whenToPreferThis":"Choose this endpoint when you need fast, cheap ($0.01 USDC) aggregate classification metrics (macro, micro, weighted) for a bounded return-reason dataset without setting up a local ML evaluation environment. Ideal for lightweight model benchmarking or spot-checking classifier quality on up to 200 records in an agentic workflow where spinning up scikit-learn or a foundation model is impractical.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:48:29.442Z","isFirstParty":false,"canonicalSlug":"return-reason-classifier-macro-micro-weighted-aggregate-scores-1312aa0e"}