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Knowledge Topic Classifier — Prediction Error Records
x402
Knowledge Topic Classifier — Prediction Error Records is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Computes prediction error diagnostics for a naive-Bayes knowledge-topic text classifier across up to 200 labelled records with actual vs. predicted category comparisons.
Price / call$0.01
Success—
StatusUnknown
How this service works
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.
Output
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.
Example use cases
Auditing a documentation classifier
“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?”
Debugging category confusion in a support bot
“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?”
Benchmarking a naive-Bayes knowledge classifier
“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?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-errors?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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