{"uid":"cap_m4LujhDSru2gTuR-aeXdd","slug":"feedback-theme-per-label-precision-and-recall-1b6dab8d","name":"Feedback Theme Per-Label Precision and Recall","description":"Feedback theme - Per-label precision and recall: Per-label precision and recall for Feedback theme. 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-feedback-theme-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":["quality","price","delivery","support"],"type":"string"},"predicted":{"enum":["quality","price","delivery","support"],"type":"string"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"quality sample document number 0 for this supplied dataset.","actual":"quality","predicted":"quality"},{"id":"example-0-1","text":"quality sample document number 1 for this supplied dataset.","actual":"quality","predicted":"quality"},{"id":"example-0-2","text":"quality sample document number 2 for this supplied dataset.","actual":"quality","predicted":"quality"},{"id":"example-1-0","text":"price sample document number 0 for this supplied dataset.","actual":"price","predicted":"price"},{"id":"example-1-1","text":"price sample document number 1 for this supplied dataset.","actual":"price","predicted":"price"},{"id":"example-1-2","text":"price sample document number 2 for this supplied dataset.","actual":"price","predicted":"price"},{"id":"example-2-0","text":"delivery sample document number 0 for this supplied dataset.","actual":"delivery","predicted":"delivery"},{"id":"example-2-1","text":"delivery sample document number 1 for this supplied dataset.","actual":"delivery","predicted":"delivery"},{"id":"example-2-2","text":"delivery sample document number 2 for this supplied dataset.","actual":"delivery","predicted":"delivery"},{"id":"example-3-0","text":"support sample document number 0 for this supplied dataset.","actual":"support","predicted":"support"},{"id":"example-3-1","text":"support sample document number 1 for this supplied dataset.","actual":"support","predicted":"support"},{"id":"example-3-2","text":"support sample document number 2 for this supplied dataset.","actual":"support","predicted":"support"}],"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_AVln2BcYJUvQyAmr5kPAI","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 feedback theme classifier across up to 200 labelled text records with actual and predicted labels.","exampleAgentPrompt":"I have 150 customer feedback records where each has the actual theme label (quality, price, delivery, or support) and what my classifier predicted — can you compute per-label precision and recall so I can see which categories my model struggles with?","exampleUseCases":[{"title":"E-commerce feedback classifier audit","prompt":"I've labelled 120 customer reviews with actual themes like quality, price, delivery, and support, and I have my model's predicted labels too — can you run per-label precision and recall so I see exactly which theme my model is most confused about?"},{"title":"Naive Bayes baseline evaluation","prompt":"I trained a simple naive Bayes model to classify feedback into quality, price, delivery, and support, and I want to evaluate it — here are 80 records with actual and predicted labels, please give me per-label precision and recall breakdowns."},{"title":"Support team classifier QA","prompt":"Our support team's auto-tagger assigns one of four feedback themes to each ticket, and I need to validate how well it's doing — I have 200 records with the correct label and the predicted label for each, can you compute precision and recall per label?"}],"resultDescription":"A per-label breakdown of precision and recall scores for each feedback theme category (quality, price, delivery, support), computed from the supplied actual vs. predicted label pairs across up to 200 records.","failureModes":["More than 200 records submitted — input will be rejected or truncated","Missing required fields (id, text, actual, predicted) in any record — validation error","Invalid label values outside the enum (quality, price, delivery, support) — schema rejection","Empty records array — no metrics computable","All records belonging to one label only — some per-label metrics undefined or zero"],"whenToPreferThis":"Choose this endpoint when you need lightweight, bounded per-label precision and recall evaluation for a feedback theme classifier without requiring a hosted model or large-scale ML infrastructure. Ideal for small dataset spot-checks (up to 200 records), local evaluation pipelines, or naive Bayes baseline comparisons across the four feedback categories (quality, price, delivery, support). Not suitable for full model training, multi-class datasets beyond these four labels, or large-scale batch evaluation.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:47:23.447Z","isFirstParty":false,"canonicalSlug":"feedback-theme-per-label-precision-and-recall-1b6dab8d"}