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Classification Model Audit
x402
Classification Model Audit is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-10).
Computes confusion matrix, per-label precision/recall/F1, macro F1, and accuracy from up to 300 supplied expected/predicted label pairs
Price / call$0.01
Success—
StatusUnknown
How this service works
Classification Model Audit: Compute a confusion matrix, per-label precision/recall/F1, macro F1 and accuracy from up to 300 supplied expected/predicted labels. No model calls or claims about unseen data. 0.01 USDC includes up to 300 labelled predictions.
Output
Returns a confusion matrix showing true/false positives and negatives per class, per-label precision, recall, and F1 scores, as well as overall macro F1 and accuracy — all computed from the supplied expected/predicted label pairs without making any model calls.
Example use cases
Sentiment classifier performance review
“I ran my sentiment classifier on 150 test samples — each one has the true label (positive, negative, neutral) and what the model predicted. Can you compute the confusion matrix and per-label precision, recall, and F1 so I can see where it's going wrong?”
Multi-class image tag audit
“I have 300 expected vs predicted category labels from my image tagger. Can you give me the full classification audit — confusion matrix, per-class F1, and the overall macro F1 and accuracy?”
Spam detection model validation
“Before shipping my spam filter, I want to validate it on my held-out test set. I have 80 expected and predicted labels — can you calculate precision, recall, F1, and accuracy so I can confirm it meets our quality bar?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-classification-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a fixed set of up to 300 ground-truth and predicted label pairs and need standard classification metrics (confusion matrix, per-label precision/recall/F1, macro F1, accuracy) computed quickly and cheaply without spinning up any model infrastructure. It is ideal for post-hoc model auditing, CI/CD quality gates, or reporting workflows where you already have predictions in hand.
Side effects
- Empty or missing examples array returns a validation error
- More than 300 label pairs submitted exceeds the limit and is rejected
- Labels with empty strings or exceeding 40 characters fail schema validation
- Mismatched or malformed JSON body returns a 400 error
- Network or payment (x402) failure prevents the call from completing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| examples | array |
Tags
x402
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