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Knowledge Topic Classifier — Macro, Micro & Weighted Scores
x402
Knowledge Topic Classifier — Macro, Micro & Weighted Scores 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 macro, micro, and weighted precision/recall/F1 scores for a knowledge-topic text classifier across four categories: setup, usage, troubleshooting, and reference.
Price / call$0.01
Success—
StatusUnknown
How this service works
Knowledge topic - Macro micro and weighted scores: Macro micro and weighted scores 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 macro micro and weighted scores deliverable.
Output
Returns aggregate classification metrics — macro, micro, and weighted precision, recall, and F1 scores — computed over up to 200 supplied records with actual and predicted labels across four knowledge topic categories: setup, usage, troubleshooting, and reference.
Example use cases
Documentation classifier quality check
“I've got 150 labelled knowledge base articles where each one is tagged as setup, usage, troubleshooting, or reference — both the ground truth and what my classifier predicted. Can you compute the macro, micro, and weighted F1 scores so I can see how well it's doing?”
Naive Bayes baseline benchmarking
“I just trained a naive Bayes model to sort support docs into setup, usage, troubleshooting, and reference categories. Here's my test set of 80 records with actual and predicted labels — give me the full macro, micro, and weighted precision, recall, and F1 breakdown.”
Knowledge topic model regression check
“Before shipping a new version of our doc classifier, I need to verify it hasn't regressed. Can you take these 200 actual-vs-predicted knowledge topic label pairs and return the macro, micro, and weighted aggregate scores?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-aggregate-scores?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cheap ($0.01 USDC) evaluation of a knowledge-topic text classifier that categorizes documents into setup, usage, troubleshooting, and reference — particularly when you already have predicted and actual labels and want macro/micro/weighted aggregate scores without setting up a local evaluation pipeline.
Side effects
- Empty records array returns error or degenerate metrics
- Records exceeding 200 items may be truncated or rejected
- Invalid or missing 'actual'/'predicted' enum values cause validation errors
- Imbalanced classes may produce misleading macro averages
- Missing required fields (id, text, actual, predicted) cause schema validation failures
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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