{"uid":"cap_1YJhxcdb2hBWrucoFY0SP","slug":"meeting-topic-label-and-prediction-audit-69918779","name":"Meeting Topic Label and Prediction Audit","description":"Meeting topic - Label and prediction audit: Label and prediction audit 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 label and prediction audit deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-meeting-topic-label-audit?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":["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 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dataset.","actual":"progress","predicted":"progress","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"progress sample document number 2 for this supplied dataset.","actual":"progress","predicted":"progress","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"decision sample document number 0 for this supplied dataset.","actual":"decision","predicted":"decision","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"decision sample document number 1 for this supplied dataset.","actual":"decision","predicted":"decision","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"decision sample document number 2 for this supplied dataset.","actual":"decision","predicted":"decision","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-3-0","text":"review sample document number 0 for this supplied dataset.","actual":"review","predicted":"review","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-1","text":"review sample document number 1 for this supplied dataset.","actual":"review","predicted":"review","probabilities":[0.06666666666666667,0.06666666666666667,0.06666666666666667,0.8]},{"id":"example-3-2","text":"review sample document number 2 for this supplied 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classification labels and predictions for meeting topic text records across four categories (planning, progress, decision, review), returning evaluation metrics and diagnostics.","exampleAgentPrompt":"Can you run a label and prediction audit on these 50 meeting transcript snippets I've labeled as planning, progress, decision, or review — I want to see accuracy metrics and where the classifier went wrong, including predicted probabilities for each record?","exampleUseCases":[{"title":"Classifier QA before production deployment","prompt":"I've got 120 labeled meeting notes where each one is tagged as planning, progress, decision, or review, and I ran them through my classifier. Can you audit the predictions against the actual labels and tell me the accuracy, any problematic categories, and where probabilities look off?"},{"title":"Detecting mislabeled training data","prompt":"Before I retrain my meeting topic model, can you audit these 80 labeled records — I suspect some of the 'review' entries were actually mislabeled as 'decision'. Give me a breakdown of where predictions diverged from actuals so I can spot annotation errors."},{"title":"Benchmarking a naive-Bayes meeting classifier","prompt":"I built a simple naive-Bayes classifier for meeting topics and have 200 text samples with their predicted labels, actual labels, and class probabilities across planning, progress, decision, and review. Can you run a prediction audit so I can see how it benchmarks and where it fails?"}],"resultDescription":"Returns a structured audit report covering classification accuracy, per-class metrics (precision, recall, F1), confusion matrix statistics, probability calibration diagnostics, and comparisons against naive-Bayes baselines for the four meeting topic categories: planning, progress, decision, and review.","failureModes":["More than 200 records submitted — payload exceeds limit","Missing required fields (id, text, actual, predicted, probabilities) on any record — validation error","Probabilities array does not contain exactly 4 values summing near 1.0 — schema rejection","Invalid enum value for actual or predicted outside allowed set — rejection","Empty records array — no audit can be performed","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have a small labeled dataset (up to 200 records) of meeting-topic text and want a local, lightweight audit of classifier quality without sending data to a hosted model or foundation model. Ideal for NLP pipeline QA, training-data validation, or benchmarking a naive-Bayes baseline on the specific planning/progress/decision/review taxonomy. Not suitable for large-scale batch evaluation or training new models.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:48:07.858Z","isFirstParty":false,"canonicalSlug":"meeting-topic-label-and-prediction-audit-69918779"}