{"uid":"cap_-1itFgivKGuZ038dyT5C4","slug":"product-category-per-label-precision-and-recall-ddcae01b","name":"Product Category Per-Label Precision and Recall","description":"Product category - Per-label precision and recall: Per-label precision and recall 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 per-label precision and recall deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-product-category-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":["apparel","home","electronics","other"],"type":"string"},"predicted":{"enum":["apparel","home","electronics","other"],"type":"string"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"apparel sample document number 0 for this supplied dataset.","actual":"apparel","predicted":"apparel"},{"id":"example-0-1","text":"apparel sample document number 1 for this supplied dataset.","actual":"apparel","predicted":"apparel"},{"id":"example-0-2","text":"apparel sample document number 2 for this supplied dataset.","actual":"apparel","predicted":"apparel"},{"id":"example-1-0","text":"home sample document number 0 for this supplied dataset.","actual":"home","predicted":"home"},{"id":"example-1-1","text":"home sample document number 1 for this supplied dataset.","actual":"home","predicted":"home"},{"id":"example-1-2","text":"home sample document number 2 for this supplied dataset.","actual":"home","predicted":"home"},{"id":"example-2-0","text":"electronics sample document number 0 for this supplied dataset.","actual":"electronics","predicted":"electronics"},{"id":"example-2-1","text":"electronics sample document number 1 for this supplied dataset.","actual":"electronics","predicted":"electronics"},{"id":"example-2-2","text":"electronics sample document number 2 for this supplied dataset.","actual":"electronics","predicted":"electronics"},{"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_4_V2LBp298ElJ5cIT514U","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 product category classifier across up to 200 labelled text records with actual and predicted labels.","exampleAgentPrompt":"Can you evaluate my product category classifier by computing per-label precision and recall? I have 50 labelled records, each with an ID, the product description text, the actual category (apparel, home, electronics, or other), and what my model predicted — I want to see how it performs on each label separately.","exampleUseCases":[{"title":"Diagnosing weak categories in e-commerce classifier","prompt":"I've run my product description classifier over 120 items and stored the actual vs predicted labels. Can you compute precision and recall separately for apparel, home, electronics, and other so I can see which categories my model struggles with most?"},{"title":"Pre-launch model evaluation for retail search","prompt":"Before we ship our product tagging model, I need a per-label precision and recall report on these 80 labelled test records — actual and predicted categories are apparel, home, electronics, or other. Can you run that evaluation?"},{"title":"Comparing classifier iterations on product data","prompt":"I retrained my product category model and have 150 labelled records with the new predictions alongside the ground truth. Give me per-label precision and recall for each of the four categories so I can see if apparel recall improved."}],"resultDescription":"A per-label breakdown of precision and recall for each product category label (apparel, home, electronics, other), computed from the supplied actual vs predicted label pairs across up to 200 text records.","failureModes":["Exceeding 200 records returns an error or truncates input","Missing required fields (id, text, actual, predicted) causes validation failure","Labels outside the allowed enum (apparel, home, electronics, other) are rejected","Empty records array returns no meaningful metrics","Highly imbalanced label sets may yield undefined precision/recall for rare labels"],"whenToPreferThis":"Choose this endpoint when you need label-level (per-class) precision and recall for a 4-class product category text classifier and have up to 200 pre-labelled records with both actual and predicted labels. It is well-suited for lightweight, local evaluation without needing a hosted model or foundation model infrastructure. Prefer it over aggregate accuracy metrics when you need to diagnose which specific product categories underperform.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:48:27.166Z","isFirstParty":false,"canonicalSlug":"product-category-per-label-precision-and-recall-ddcae01b"}