{"uid":"cap_1WTiqmZxFOHtijJtOloH6","slug":"product-category-label-and-prediction-audit-f414c2ad","name":"Product Category Label and Prediction Audit","description":"Product category - Label and prediction audit: Label and prediction audit for Product category. 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-product-category-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":["apparel","home","electronics","other"],"type":"string"},"predicted":{"enum":["apparel","home","electronics","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":"apparel sample document number 0 for this supplied dataset.","actual":"apparel","predicted":"apparel","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"apparel sample document number 1 for this supplied dataset.","actual":"apparel","predicted":"apparel","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"apparel sample document number 2 for this supplied dataset.","actual":"apparel","predicted":"apparel","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"home sample document number 0 for this supplied dataset.","actual":"home","predicted":"home","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"home sample document number 1 for this supplied dataset.","actual":"home","predicted":"home","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"home sample document number 2 for this supplied dataset.","actual":"home","predicted":"home","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"electronics sample document number 0 for this supplied dataset.","actual":"electronics","predicted":"electronics","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"electronics sample document number 1 for this supplied dataset.","actual":"electronics","predicted":"electronics","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"electronics sample document number 2 for this supplied dataset.","actual":"electronics","predicted":"electronics","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_iX9a7jShebdAd3r_B5h98","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 a product category classifier (apparel, home, electronics, other), computing evaluation metrics and naive-Bayes baselines.","exampleAgentPrompt":"Can you audit these 150 product text records I have labelled as apparel, home, electronics, or other — I have the actual labels, the predicted labels, and the class probabilities for each — and give me a full evaluation report with accuracy metrics and a naive-Bayes baseline comparison?","exampleUseCases":[{"title":"E-commerce classifier quality check","prompt":"I've got 200 product listings where I recorded the actual category (apparel, home, electronics, or other), what my classifier predicted, and the probability scores for each class. Can you run a label and prediction audit and tell me where it's going wrong?"},{"title":"Identifying misclassified apparel records","prompt":"My product classifier keeps mixing up apparel and home items — can you audit these 80 labelled records with their predicted labels and probabilities and show me where the confusion is happening compared to a naive-Bayes baseline?"},{"title":"Pre-deployment model evaluation","prompt":"Before I ship this product category model to production, can you run an audit on these 120 sample records — I have the ground-truth labels, model predictions, and four-class probability vectors for each — and give me precision, recall, and a baseline comparison?"}],"resultDescription":"A bounded audit deliverable covering: label-vs-prediction agreement statistics, per-class accuracy and error breakdown, confusion analysis across apparel/home/electronics/other, probability calibration insights, and a naive-Bayes baseline comparison for the supplied dataset of up to 200 records.","failureModes":["More than 200 records submitted — exceeds batch limit","Probabilities array does not contain exactly 4 values per record","Probability values outside 0–1 range","Missing required fields (id, text, actual, predicted, probabilities)","actual or predicted values outside the allowed enum (apparel, home, electronics, other)","Empty records array submitted","Payment not received — x402 payment required before response"],"whenToPreferThis":"Choose this endpoint when you have a small labelled dataset (up to 200 records) for a four-class product category text classifier and need a local audit with evaluation metrics and a naive-Bayes baseline — without requiring a hosted model or foundation model training. Ideal for rapid pre-deployment checks, debugging classifier errors, or validating label quality on e-commerce product data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T00:50:30.016Z","isFirstParty":false,"canonicalSlug":"product-category-label-and-prediction-audit-f414c2ad"}