# Ticket Priority Classifier — Per-Label Precision and Recall

> Ticket Priority Classifier — Per-Label Precision and Recall 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 per-label precision and recall metrics for a ticket priority classifier across low, normal, and high priority classes using up to 200 labeled text records.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-ticket-priority-per-label?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/ticket-priority-classifier-per-label-precision-and-recall-8d17c4f9
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_5kV-qLi8jjG3HPU4BR_Mt

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability ticket-priority-classifier-per-label-precision-and-recall-8d17c4f9 -d '<json body>'
```

Example prompt: Can you evaluate my ticket priority classifier by computing per-label precision and recall? I have 150 support ticket records each with the ticket text, actual priority label (low, normal, or high), and my model's predicted label — I want to see how well it performs on each priority class separately.

## When to prefer this

Choose this endpoint when you need a granular, per-class evaluation of a ticket priority classifier — specifically when you want to distinguish precision and recall independently for each of the low, normal, and high priority labels rather than just an overall accuracy or macro average. Ideal for diagnosing which priority class a model under-performs on, validating classifier changes, or auditing triage automation quality. Best suited for small to medium evaluation sets (up to 200 records) where you supply your own ground truth and predictions.

## Known failure modes

- Fewer than 2 records supplied — minimum record count not met
- Records missing required fields (id, text, actual, predicted)
- Label values outside allowed enum (low, normal, high)
- More than 200 records submitted — exceeds maximum batch size
- Record ID exceeds 80 character limit or is empty
- Text field exceeds 1000 character limit per record
- Payment not completed — x402 payment required at $0.01 USDC

## How this service works

Ticket priority - Per-label precision and recall: Per-label precision and recall for Ticket priority. 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 per-label precision and recall deliverable.

## Output

Returns per-label precision and recall scores for each of the three ticket priority classes (low, normal, high), computed from up to 200 supplied records containing ticket text, actual labels, and predicted labels. Useful for diagnosing class-specific model weaknesses.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "records": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "id",
     "text",
     "actual",
     "predicted"
    ],
    "properties": {
     "id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "text": {
      "type": "string",
      "default": "",
      "maxLength": 1000
     },
     "actual": {
      "enum": [
       "low",
       "normal",
       "high"
      ],
      "type": "string"
     },
     "predicted": {
      "enum": [
       "low",
       "normal",
       "high"
      ],
      "type": "string"
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "id": "example-0-0",
     "text": "low sample document number 0 for this supplied dataset.",
     "actual": "low",
     "predicted": "low"
    },
    {
     "id": "example-0-1",
     "text": "low sample document number 1 for this supplied dataset.",
     "actual": "low",
     "predicted": "low"
    },
    {
     "id": "example-0-2",
     "text": "low sample document number 2 for this supplied dataset.",
     "actual": "low",
     "predicted": "low"
    },
    {
     "id": "example-1-0",
     "text": "normal sample document number 0 for this supplied dataset.",
     "actual": "normal",
     "predicted": "normal"
    },
    {
     "id": "example-1-1",
     "text": "normal sample document number 1 for this supplied dataset.",
     "actual": "normal",
     "predicted": "normal"
    },
    {
     "id": "example-1-2",
     "text": "normal sample document number 2 for this supplied dataset.",
     "actual": "normal",
     "predicted": "normal"
    },
    {
     "id": "example-2-0",
     "text": "high sample document number 0 for this supplied dataset.",
     "actual": "high",
     "predicted": "high"
    },
    {
     "id": "example-2-1",
     "text": "high sample document number 1 for this supplied dataset.",
     "actual": "high",
     "predicted": "high"
    },
    {
     "id": "example-2-2",
     "text": "high sample document number 2 for this supplied dataset.",
     "actual": "high",
     "predicted": "high"
    }
   ],
   "maxItems": 200,
   "minItems": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/ticket-priority-classifier-per-label-precision-and-recall-8d17c4f9/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)