# Training Question Per-Label Precision and Recall

> Training Question 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 2026-10-28).

Computes per-label precision and recall metrics for a training question classifier using up to 200 supplied labelled text records with actual and predicted labels.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-training-question-per-label?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/training-question-per-label-precision-and-recall-6d817e13
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_b_u6syDyY6m-M_h63Gg5x

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 training-question-per-label-precision-and-recall-6d817e13 -d '<json body>'
```

Example prompt: Can you compute the per-label precision and recall for my training question classifier? I have 150 labelled records where each one has an actual label and a predicted label — categories are concept, procedure, example, and assessment.

## When to prefer this

Choose this endpoint when you need lightweight, per-label precision and recall evaluation for a 4-class training question classifier (concept, procedure, example, assessment) on small datasets up to 200 records, without needing to spin up a hosted model or use a foundation model. Ideal for quick local evaluations, baseline comparisons, or auditing label-level classifier performance cheaply at $0.01 per call.

## Known failure modes

- Exceeding the 200-record limit may result in rejection or truncation
- Missing required fields (id, text, actual, predicted) in any record will cause a validation error
- Invalid label values outside the allowed enum (concept, procedure, example, assessment) will be rejected
- Empty records array will return no meaningful metrics
- Payment failure or insufficient USDC balance will block the request

## How this service works

Training question - Per-label precision and recall: Per-label precision and recall for Training question. 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

A per-label breakdown of precision and recall metrics for each of the four label categories (concept, procedure, example, assessment), computed from the supplied records containing actual and predicted label pairs. Returned as a bounded deliverable covering up to 200 records.

## 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": [
       "concept",
       "procedure",
       "example",
       "assessment"
      ],
      "type": "string"
     },
     "predicted": {
      "enum": [
       "concept",
       "procedure",
       "example",
       "assessment"
      ],
      "type": "string"
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "id": "example-0-0",
     "text": "concept sample document number 0 for this supplied dataset.",
     "actual": "concept",
     "predicted": "concept"
    },
    {
     "id": "example-0-1",
     "text": "concept sample document number 1 for this supplied dataset.",
     "actual": "concept",
     "predicted": "concept"
    },
    {
     "id": "example-0-2",
     "text": "concept sample document number 2 for this supplied dataset.",
     "actual": "concept",
     "predicted": "concept"
    },
    {
     "id": "example-1-0",
     "text": "procedure sample document number 0 for this supplied dataset.",
     "actual": "procedure",
     "predicted": "procedure"
    },
    {
     "id": "example-1-1",
     "text": "procedure sample document number 1 for this supplied dataset.",
     "actual": "procedure",
     "predicted": "procedure"
    },
    {
     "id": "example-1-2",
     "text": "procedure sample document number 2 for this supplied dataset.",
     "actual": "procedure",
     "predicted": "procedure"
    },
    {
     "id": "example-2-0",
     "text": "example sample document number 0 for this supplied dataset.",
     "actual": "example",
     "predicted": "example"
    },
    {
     "id": "example-2-1",
     "text": "example sample document number 1 for this supplied dataset.",
     "actual": "example",
     "predicted": "example"
    },
    {
     "id": "example-2-2",
     "text": "example sample document number 2 for this supplied dataset.",
     "actual": "example",
     "predicted": "example"
    },
    {
     "id": "example-3-0",
     "text": "assessment sample document number 0 for this supplied dataset.",
     "actual": "assessment",
     "predicted": "assessment"
    },
    {
  
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/training-question-per-label-precision-and-recall-6d817e13/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)