{"uid":"cap_3fP1qFLXUJAK6RQWBvc1b","slug":"knowledge-topic-classifier-prediction-error-records-8a90a7b0","name":"Knowledge Topic Classifier — Prediction Error Records","description":"Knowledge topic - Prediction error records: Prediction error records for Knowledge 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 prediction error records deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-errors?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":["setup","usage","troubleshooting","reference"],"type":"string"},"predicted":{"enum":["setup","usage","troubleshooting","reference"],"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":"setup sample document number 0 for this supplied dataset.","actual":"setup","predicted":"setup","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-1","text":"setup sample document number 1 for this supplied dataset.","actual":"setup","predicted":"setup","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-0-2","text":"setup sample document number 2 for this supplied dataset.","actual":"setup","predicted":"setup","probabilities":[0.8,0.06666666666666667,0.06666666666666667,0.06666666666666667]},{"id":"example-1-0","text":"usage sample document number 0 for this supplied dataset.","actual":"usage","predicted":"usage","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-1","text":"usage sample document number 1 for this supplied dataset.","actual":"usage","predicted":"usage","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-1-2","text":"usage sample document number 2 for this supplied dataset.","actual":"usage","predicted":"usage","probabilities":[0.06666666666666667,0.8,0.06666666666666667,0.06666666666666667]},{"id":"example-2-0","text":"troubleshooting sample document number 0 for this supplied dataset.","actual":"troubleshooting","predicted":"troubleshooting","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-1","text":"troubleshooting sample document number 1 for this supplied dataset.","actual":"troubleshooting","predicted":"troubleshooting","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-2-2","text":"troubleshooting sample document number 2 for this supplied dataset.","actual":"troubleshooting","predicted":"troubleshooting","probabilities":[0.06666666666666667,0.06666666666666667,0.8,0.06666666666666667]},{"id":"example-3-0","text":"reference sample document number 0 for this supplied 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prediction error diagnostics for a naive-Bayes knowledge-topic text classifier across up to 200 labelled records with actual vs. predicted category comparisons.","exampleAgentPrompt":"I have 50 labelled knowledge-base articles with actual and predicted categories (setup, usage, troubleshooting, or reference) plus the 4-class probability scores for each — can you run prediction error analysis on them and tell me which records the classifier got wrong?","exampleUseCases":[{"title":"Auditing a documentation classifier","prompt":"I've got 120 knowledge article records where each one has its actual topic label (setup, usage, troubleshooting, or reference), the model's predicted label, and the four class probabilities — can you compute prediction error records so I can see exactly where the classifier is failing?"},{"title":"Debugging category confusion in a support bot","prompt":"My support bot's knowledge-topic model keeps mixing up troubleshooting and reference articles — I have 80 labelled records with actual vs predicted labels and probability arrays for each. Can you run prediction error analysis on them so I see the specific misclassified cases?"},{"title":"Benchmarking a naive-Bayes knowledge classifier","prompt":"Before deploying my knowledge-base classifier, I want to evaluate it on 200 held-out labelled records across setup, usage, troubleshooting, and reference categories. Each record has the actual label, predicted label, and four probability scores — can you generate the prediction error report?"}],"resultDescription":"A structured deliverable containing per-record prediction error analysis for up to 200 knowledge-topic text records, indicating which records were misclassified (actual vs. predicted label mismatches), associated probability distributions, and diagnostic information to support model evaluation across the four categories: setup, usage, troubleshooting, and reference.","failureModes":["More than 200 records supplied — batch is rejected or truncated","Probability arrays not summing correctly or containing values outside [0,1]","Missing required fields (id, text, actual, predicted, probabilities) cause validation errors","Invalid enum values for actual or predicted labels outside the four permitted categories","Empty records array returns no meaningful error output","Probability array not exactly 4 elements causes schema rejection"],"whenToPreferThis":"Choose this endpoint when you need per-record prediction error diagnostics for a text classification model operating over the four knowledge-topic categories (setup, usage, troubleshooting, reference) using a naive-Bayes or comparable baseline approach, with a local dataset of up to 200 labelled records. It is ideal for lightweight model evaluation without requiring a hosted model or foundation-model infrastructure. Prefer it over generic ML evaluation services when your dataset is small, your task is knowledge-base topic classification, and you want a bounded, low-cost ($0.01 USDC) evaluation deliverable.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T18:48:46.551Z","isFirstParty":false,"canonicalSlug":"knowledge-topic-classifier-prediction-error-records-8a90a7b0"}