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Commerce Intent Confusion Matrix
x402
Commerce Intent Confusion Matrix is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Computes a confusion matrix for a commerce intent classifier (browse/purchase/return/other) given up to 200 labelled and predicted text records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Commerce intent - Confusion matrix: Confusion matrix for Commerce intent. 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 confusion matrix deliverable.
Output
A confusion matrix showing true vs predicted counts across the four commerce intent classes (browse, purchase, return, other), enabling identification of misclassification patterns and per-class accuracy for up to 200 supplied labelled records.
Example use cases
Ecommerce chatbot intent model audit
“I built a classifier that tags customer messages as browse, purchase, return, or other, and I want to see where it's going wrong. Can you compute a confusion matrix using these 120 labelled records with my model's actual vs predicted labels?”
Naive Bayes baseline benchmark
“I trained a quick naive Bayes model to classify shopping intent and want to evaluate it. Run a confusion matrix on these 80 test records — each has the text, the true intent label (browse, purchase, return, or other), and what the model predicted.”
Return vs purchase misclassification check
“My commerce intent model keeps confusing returns with purchases. Can you build a confusion matrix from these 150 labelled records so I can see the exact misclassification counts between return and purchase intents?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-commerce-intent-confusion?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, low-cost confusion matrix computation for a commerce-specific intent classifier with exactly four classes (browse, purchase, return, other), and your dataset is 200 records or fewer. It is ideal for evaluating naive Bayes baselines or lightweight local classifiers without needing a hosted model or large-scale ML infrastructure.
Side effects
- More than 200 records submitted — exceeds the bounded limit
- Records missing required fields (id, text, actual, or predicted)
- Invalid enum values for actual or predicted (must be browse, purchase, return, or other)
- Empty records array submitted
- Payment of 0.01 USDC not completed — 402 response returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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