# Product Category Classifier — Prediction Error Records

> Product Category Classifier — Prediction Error Records is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).

Computes prediction error records (e.g. misclassification details, per-record loss) for a naive-Bayes product category text classifier across apparel, home, electronics, and other categories.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-product-category-errors?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/product-category-classifier-prediction-error-records-0870668b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_WNK8jaSwywj_baBOHOe8f

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-classifier-prediction-error-records-0870668b -d '<json body>'
```

Example prompt: Here are up to 200 labelled product records — each with the text, the actual category (apparel, home, electronics, or other), the predicted category, and the four class probabilities — can you run them through the prediction error records tool and show me which ones were misclassified?

## When to prefer this

Choose this endpoint when you need per-record prediction error diagnostics for a four-class product category text classifier (apparel, home, electronics, other) and already have labelled data with predicted outputs and class probabilities. It is ideal for model evaluation and debugging pipelines where you supply your own predictions and want structured error records rather than aggregate metrics. Prefer alternatives for aggregate metrics (precision/recall), multi-class problems beyond these four categories, or when you need a hosted model to generate predictions rather than just evaluate them.

## Known failure modes

- Records array exceeds 200 items — request will be rejected or truncated
- Probabilities array does not contain exactly 4 values — validation error
- Probability values outside [0,1] range — schema validation failure
- Missing required fields (id, text, actual, predicted, probabilities) — 400 error
- Invalid category enum value not in [apparel, home, electronics, other] — schema rejection
- Empty records array — may return empty or error response

## How this service works

Product category - Prediction error records: Prediction error records 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 prediction error records deliverable.

## Output

A bounded deliverable containing prediction error records for each submitted labelled text record — detailing which records were mispredicted, the actual versus predicted category labels (apparel, home, electronics, other), and associated probability information, computed using local naive-Bayes evaluation tools without a hosted model.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "records": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "id",
     "text",
     "actual",
     "predicted",
     "probabilities"
    ],
    "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"
     },
     "probabilities": {
      "type": "array",
      "items": {
       "type": "number",
       "default": 0,
       "maximum": 1,
       "minimum": 0
      },
      "default": [],
      "maxItems": 4,
      "minItems": 4
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "id": "example-0-0",
     "text": "apparel sample document number 0 for this supplied dataset.",
     "actual": "apparel",
     "predicted": "apparel",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-0-1",
     "text": "apparel sample document number 1 for this supplied dataset.",
     "actual": "apparel",
     "predicted": "apparel",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-0-2",
     "text": "apparel sample document number 2 for this supplied dataset.",
     "actual": "apparel",
     "predicted": "apparel",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-0",
     "text": "home sample document number 0 for this supplied dataset.",
     "actual": "home",
     "predicted": "home",
     "probabilities": [
      0.06666666666666667,
      0.8,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-1",
     "text": "home sample document number 1 for this supplied dataset.",
     "actual": "home",
     "predicted": "home",
     "probabilities": [
      0.06666666666666667,
      0.8,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-2",
     "text"
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/product-category-classifier-prediction-error-records-0870668b/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
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