{"uid":"cap_b_u6syDyY6m-M_h63Gg5x","slug":"training-question-per-label-precision-and-recall-6d817e13","name":"Training Question Per-Label Precision and Recall","description":"Training question - Per-label precision and recall: Per-label precision and recall for Training question. 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-training-question-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":["concept","procedure","example","assessment"],"type":"string"},"predicted":{"enum":["concept","procedure","example","assessment"],"type":"string"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"concept sample document number 0 for this supplied dataset.","actual":"concept","predicted":"concept"},{"id":"example-0-1","text":"concept sample document number 1 for this supplied dataset.","actual":"concept","predicted":"concept"},{"id":"example-0-2","text":"concept sample document number 2 for this supplied dataset.","actual":"concept","predicted":"concept"},{"id":"example-1-0","text":"procedure sample document number 0 for this supplied dataset.","actual":"procedure","predicted":"procedure"},{"id":"example-1-1","text":"procedure sample document number 1 for this supplied dataset.","actual":"procedure","predicted":"procedure"},{"id":"example-1-2","text":"procedure sample document number 2 for this supplied dataset.","actual":"procedure","predicted":"procedure"},{"id":"example-2-0","text":"example sample document number 0 for this supplied dataset.","actual":"example","predicted":"example"},{"id":"example-2-1","text":"example sample document number 1 for this supplied dataset.","actual":"example","predicted":"example"},{"id":"example-2-2","text":"example sample document number 2 for this supplied dataset.","actual":"example","predicted":"example"},{"id":"example-3-0","text":"assessment sample document number 0 for this supplied dataset.","actual":"assessment","predicted":"assessment"},{"id":"example-3-1","text":"assessment sample document number 1 for this supplied dataset.","actual":"assessment","predicted":"assessment"},{"id":"example-3-2","text":"assessment sample document number 2 for this supplied dataset.","actual":"assessment","predicted":"assessment"}],"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_bgGOgPYqOUsSe6Py6POLO","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 training question classifier using up to 200 supplied labelled text records with actual and predicted labels.","exampleAgentPrompt":"Can you compute the per-label precision and recall for my training question classifier? I have 150 labelled records where each one has an actual label and a predicted label — categories are concept, procedure, example, and assessment.","exampleUseCases":[{"title":"Evaluating educational content classifier","prompt":"I've got 120 labelled training question records — each tagged as either concept, procedure, example, or assessment — along with my model's predictions. Can you calculate precision and recall for each label so I can see where my classifier is weakest?"},{"title":"Identifying underperforming label categories","prompt":"My text classifier seems to confuse 'procedure' and 'example' labels a lot. I have 80 records with actual and predicted labels — can you give me a per-label precision and recall breakdown so I can pinpoint which categories are dragging down performance?"},{"title":"Benchmarking a naive Bayes baseline model","prompt":"I'm running a baseline naive Bayes model on 200 training question records across four labels: concept, procedure, example, and assessment. Can you compute the per-label precision and recall so I can document the baseline metrics before trying more advanced models?"}],"resultDescription":"A per-label breakdown of precision and recall metrics for each of the four label categories (concept, procedure, example, assessment), computed from the supplied records containing actual and predicted label pairs. Returned as a bounded deliverable covering up to 200 records.","failureModes":["Exceeding the 200-record limit may result in rejection or truncation","Missing required fields (id, text, actual, predicted) in any record will cause a validation error","Invalid label values outside the allowed enum (concept, procedure, example, assessment) will be rejected","Empty records array will return no meaningful metrics","Payment failure or insufficient USDC balance will block the request"],"whenToPreferThis":"Choose this endpoint when you need lightweight, per-label precision and recall evaluation for a 4-class training question classifier (concept, procedure, example, assessment) on small datasets up to 200 records, without needing to spin up a hosted model or use a foundation model. Ideal for quick local evaluations, baseline comparisons, or auditing label-level classifier performance cheaply at $0.01 per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:36.136Z","isFirstParty":false,"canonicalSlug":"training-question-per-label-precision-and-recall-6d817e13"}