# Product Category Per-Label Precision and Recall

> Product Category 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 2026-10-15).

Computes per-label precision and recall metrics for a product category classifier across up to 200 labelled text records with actual and predicted labels.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-product-category-per-label?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/product-category-per-label-precision-and-recall-ddcae01b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_-1itFgivKGuZ038dyT5C4

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 product-category-per-label-precision-and-recall-ddcae01b -d '<json body>'
```

Example prompt: Can you evaluate my product category classifier by computing per-label precision and recall? I have 50 labelled records, each with an ID, the product description text, the actual category (apparel, home, electronics, or other), and what my model predicted — I want to see how it performs on each label separately.

## When to prefer this

Choose this endpoint when you need label-level (per-class) precision and recall for a 4-class product category text classifier and have up to 200 pre-labelled records with both actual and predicted labels. It is well-suited for lightweight, local evaluation without needing a hosted model or foundation model infrastructure. Prefer it over aggregate accuracy metrics when you need to diagnose which specific product categories underperform.

## Known failure modes

- Exceeding 200 records returns an error or truncates input
- Missing required fields (id, text, actual, predicted) causes validation failure
- Labels outside the allowed enum (apparel, home, electronics, other) are rejected
- Empty records array returns no meaningful metrics
- Highly imbalanced label sets may yield undefined precision/recall for rare labels

## How this service works

Product category - Per-label precision and recall: Per-label precision and recall for Product category. 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 for each product category label (apparel, home, electronics, other), computed from the supplied actual vs predicted label pairs across up to 200 text 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": [
       "apparel",
       "home",
       "electronics",
       "other"
      ],
      "type": "string"
     },
     "predicted": {
      "enum": [
       "apparel",
       "home",
       "electronics",
       "other"
      ],
      "type": "string"
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "id": "example-0-0",
     "text": "apparel sample document number 0 for this supplied dataset.",
     "actual": "apparel",
     "predicted": "apparel"
    },
    {
     "id": "example-0-1",
     "text": "apparel sample document number 1 for this supplied dataset.",
     "actual": "apparel",
     "predicted": "apparel"
    },
    {
     "id": "example-0-2",
     "text": "apparel sample document number 2 for this supplied dataset.",
     "actual": "apparel",
     "predicted": "apparel"
    },
    {
     "id": "example-1-0",
     "text": "home sample document number 0 for this supplied dataset.",
     "actual": "home",
     "predicted": "home"
    },
    {
     "id": "example-1-1",
     "text": "home sample document number 1 for this supplied dataset.",
     "actual": "home",
     "predicted": "home"
    },
    {
     "id": "example-1-2",
     "text": "home sample document number 2 for this supplied dataset.",
     "actual": "home",
     "predicted": "home"
    },
    {
     "id": "example-2-0",
     "text": "electronics sample document number 0 for this supplied dataset.",
     "actual": "electronics",
     "predicted": "electronics"
    },
    {
     "id": "example-2-1",
     "text": "electronics sample document number 1 for this supplied dataset.",
     "actual": "electronics",
     "predicted": "electronics"
    },
    {
     "id": "example-2-2",
     "text": "electronics sample document number 2 for this supplied dataset.",
     "actual": "electronics",
     "predicted": "electronics"
    },
    {
     "id": "example-3-0",
     "text": "other sample document number 0 for this supplied dataset.",
     "actual": "other",
     "predicted": "other"
    },
    {
     "id": "example-3-1",
     "text":
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/product-category-per-label-precision-and-recall-ddcae01b/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)