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Lead Stage Supplied-Group ML Evaluation
x402
Lead Stage Supplied-Group ML Evaluation 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).
Evaluates a user-supplied set of labelled lead-stage text records by group, returning per-group classification metrics and naive-Bayes baselines for the enquiry/qualified/negotiation/closed taxonomy.
Price / call$0.01
Success—
StatusUnknown
How this service works
Lead stage - Supplied-group evaluation: Supplied-group evaluation for Lead stage. 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 supplied-group evaluation deliverable.
Output
Returns per-group classification evaluation metrics for the supplied labelled records, including accuracy and other performance statistics broken down by the group identifier, along with a naive-Bayes baseline for comparison. The deliverable is a bounded evaluation report covering up to 200 records using local dataset tools — no hosted model or foundation-model training is involved.
Example use cases
Regional bias audit for lead scoring
“I want to check whether my lead stage classifier performs equally well across different sales regions. I have 150 labelled records — each has an ID, the lead text, the actual label (enquiry, qualified, negotiation, or closed), my model's predicted label, and a group tag for the region like 'north' or 'south'. Run the group evaluation and tell me which region has the worst accuracy.”
A/B model comparison by customer segment
“We ran two versions of our lead classifier and tagged each record with which customer segment it belongs to — 'SMB' or 'Enterprise'. I have 180 records with IDs, lead text, actual stages, predicted stages, and segment labels. Can you do a supplied-group evaluation so I can see the per-segment metrics and whether there's a meaningful difference?”
Baseline check for a new lead pipeline
“Before we go live with our new lead scoring model, I want to see how its predictions compare to a naive-Bayes baseline, broken down by product line group. I have about 120 records — each has an ID, the original lead text, the correct stage label (enquiry, qualified, negotiation, or closed), the model's predicted label, and the product line group like 'cloud' or 'on-prem'. Can you run the group evaluation report?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-lead-stage-group-evaluation?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a pre-labelled set of lead-stage text records with both actual and predicted class labels and you want group-stratified evaluation metrics and a naive-Bayes baseline — without needing to train or host a model. It is ideal for auditing group fairness, diagnosing per-segment model weaknesses, or benchmarking a simple classifier across the four-stage lead pipeline taxonomy (enquiry, qualified, negotiation, closed). Prefer alternatives if you need to train a model, perform inference on unlabelled data, or evaluate more than 200 records at once.
Side effects
- Records array exceeds 200 items — request will be rejected or truncated
- Missing required fields (id, text, actual, predicted, group) on any record causes validation error
- Invalid enum value for actual or predicted (must be exactly enquiry, qualified, negotiation, or closed)
- Empty records array returns no meaningful metrics
- Text field exceeding 1000 characters per record may be truncated or rejected
- Group field or ID exceeding 80 characters causes schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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