{"uid":"cap_dRbxB4daX7OADZyInuBqq","slug":"meeting-topic-classifier-top-k-label-coverage-9358c023","name":"Meeting Topic Classifier – Top-k Label Coverage","description":"Meeting topic - Top-k label coverage: Top-k label coverage for Meeting topic. 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 top-k label coverage deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-meeting-topic-top-k?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"k":{"type":"integer","default":2,"maximum":4,"minimum":1},"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":["planning","progress","decision","review"],"type":"string"},"predicted":{"enum":["planning","progress","decision","review"],"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":"planning sample document number 0 for this supplied dataset.","actual":"planning","predicted":"planning","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"planning sample document number 1 for this supplied dataset.","actual":"planning","predicted":"planning","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"planning sample document number 2 for this supplied dataset.","actual":"planning","predicted":"planning","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"progress sample document number 0 for this supplied dataset.","actual":"progress","predicted":"progress","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"progress sample document number 1 for this supplied 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top-k label coverage metrics for a naive-Bayes meeting topic classifier across up to 200 labelled text records with 4 categories: planning, progress, decision, review.","exampleAgentPrompt":"Evaluate the top-2 label coverage for my meeting topic classifier — here are 12 labelled records, each with the actual category (planning, progress, decision, or review), the predicted label, and 4 class probabilities per record.","exampleUseCases":[{"title":"Validate meeting classifier before deployment","prompt":"Before I ship my meeting topic model, can you compute the top-2 label coverage across these 50 labelled meeting transcripts? Each record has the actual topic (planning, progress, decision, or review), the model's top prediction, and its 4 class probability scores."},{"title":"Compare k=1 vs k=2 coverage for model selection","prompt":"I'm choosing between two meeting classifiers and want to see their top-1 label coverage using these 80 test records with actual labels and predicted probabilities across planning, progress, decision, and review — set k to 1."},{"title":"Audit naive-Bayes baseline on small meeting dataset","prompt":"Can you compute top-3 label coverage for a naive-Bayes meeting topic baseline I trained? I have 30 labelled records with actual and predicted categories and all 4 class probabilities — the classes are planning, progress, decision, and review."}],"resultDescription":"Returns a top-k label coverage score indicating the proportion of records where the true label appears among the top-k highest-probability predicted classes, computed over the supplied batch of up to 200 meeting topic records.","failureModes":["More than 200 records submitted — batch size limit exceeded","Probabilities array length not exactly 4 for each record","k value outside allowed range of 1–4","Label values outside the allowed enum (planning, progress, decision, review)","Malformed record missing required fields (id, text, actual, predicted, probabilities)","Probabilities not summing to approximately 1.0 per record"],"whenToPreferThis":"Choose this endpoint when you need a fast, cheap ($0.01 USDC) evaluation metric for a 4-class meeting topic classifier and already have model predictions and probabilities in hand. It is ideal for small local datasets (up to 200 records) where you want to measure top-k coverage without training or calling a hosted model. Prefer it over full retraining pipelines or foundation-model APIs when the goal is purely metric computation on pre-classified records.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:48:15.855Z","isFirstParty":false,"canonicalSlug":"meeting-topic-classifier-top-k-label-coverage-9358c023"}