{"uid":"cap_WNK8jaSwywj_baBOHOe8f","slug":"product-category-classifier-prediction-error-records-0870668b","name":"Product Category Classifier — Prediction Error Records","description":"Product category - Prediction error records: Prediction error records 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 prediction error records deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-product-category-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":["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_QO2kvtkcZ__RzLstIUUTe","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 (e.g. misclassification details, per-record loss) for a naive-Bayes product category text classifier across apparel, home, electronics, and other categories.","exampleAgentPrompt":"Here are up to 200 labelled product records — each with the text, the actual category (apparel, home, electronics, or other), the predicted category, and the four class probabilities — can you run them through the prediction error records tool and show me which ones were misclassified?","exampleUseCases":[{"title":"Auditing apparel misclassifications","prompt":"I have 150 labelled product descriptions with actual and predicted categories plus probability scores — run them through the prediction error analysis and tell me which apparel items were predicted as something else."},{"title":"Model debugging for ecommerce team","prompt":"Our naive-Bayes product classifier is underperforming; here are 200 records with their actual labels (apparel, home, electronics, other), predicted labels, and probabilities — give me the full prediction error records so we can see where it's going wrong."},{"title":"Pre-launch classifier validation","prompt":"Before we ship this product categorization model, I want to check its errors on our 80-record validation set — each record has an ID, text, actual category, predicted category, and four class probabilities. Can you produce the prediction error records for me?"}],"resultDescription":"A bounded deliverable containing prediction error records for each submitted labelled text record — detailing which records were mispredicted, the actual versus predicted category labels (apparel, home, electronics, other), and associated probability information, computed using local naive-Bayes evaluation tools without a hosted model.","failureModes":["Records array exceeds 200 items — request will be rejected or truncated","Probabilities array does not contain exactly 4 values — validation error","Probability values outside [0,1] range — schema validation failure","Missing required fields (id, text, actual, predicted, probabilities) — 400 error","Invalid category enum value not in [apparel, home, electronics, other] — schema rejection","Empty records array — may return empty or error response"],"whenToPreferThis":"Choose this endpoint when you need per-record prediction error diagnostics for a four-class product category text classifier (apparel, home, electronics, other) and already have labelled data with predicted outputs and class probabilities. It is ideal for model evaluation and debugging pipelines where you supply your own predictions and want structured error records rather than aggregate metrics. Prefer alternatives for aggregate metrics (precision/recall), multi-class problems beyond these four categories, or when you need a hosted model to generate predictions rather than just evaluate them.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T00:53:42.347Z","isFirstParty":false,"canonicalSlug":"product-category-classifier-prediction-error-records-0870668b"}