# Return Reason Prediction Error Records

> Return Reason 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 2026-10-19).

Computes prediction error records for a naive-Bayes return-reason classifier on up to 200 supplied labelled text records

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-errors?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/return-reason-prediction-error-records-93f18923
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Nz54UZd3L8cGGi18a37jw

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 return-reason-prediction-error-records-93f18923 -d '<json body>'
```

Example prompt: I have up to 200 labelled return-reason records — each with an ID, the customer text, the actual class (damaged, wrong_item, unwanted, or other), the predicted class, and the four class probabilities — can you run them through the prediction error records tool so I can see exactly which records my model got wrong?

## When to prefer this

Choose this endpoint when you have a pre-labeled dataset for return-reason classification (with actual and predicted labels plus class probabilities) and need per-record error diagnostics from a lightweight naive-Bayes baseline evaluation — not a hosted model or foundation-model fine-tuning service. Ideal for small-scale offline evaluation, auditing classifier output, or rapid error analysis without spinning up your own ML infrastructure.

## Known failure modes

- Records array exceeds 200 items — request rejected
- Missing required fields (id, text, actual, predicted, probabilities) on any record — validation error
- Probabilities array does not contain exactly 4 values between 0 and 1 — schema error
- Invalid enum value for actual or predicted (must be damaged, wrong_item, unwanted, or other) — rejection
- Payment of 0.01 USDC not completed via x402 — 402 Payment Required

## How this service works

Return reason - Prediction error records: Prediction error records for Return reason. 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 set of prediction error records for each supplied labelled text item, identifying where the predicted return-reason class differed from the actual class, along with supporting probability information per record — up to 200 records processed in one call.

## 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": [
       "damaged",
       "wrong_item",
       "unwanted",
       "other"
      ],
      "type": "string"
     },
     "predicted": {
      "enum": [
       "damaged",
       "wrong_item",
       "unwanted",
       "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": "damaged sample document number 0 for this supplied dataset.",
     "actual": "damaged",
     "predicted": "damaged",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-0-1",
     "text": "damaged sample document number 1 for this supplied dataset.",
     "actual": "damaged",
     "predicted": "damaged",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-0-2",
     "text": "damaged sample document number 2 for this supplied dataset.",
     "actual": "damaged",
     "predicted": "damaged",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-0",
     "text": "wrong_item sample document number 0 for this supplied dataset.",
     "actual": "wrong_item",
     "predicted": "wrong_item",
     "probabilities": [
      0.06666666666666667,
      0.8,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-1",
     "text": "wrong_item sample document number 1 for this supplied dataset.",
     "actual": "wrong_item",
     "predicted": "wrong_item",
     "probabilities": [
      0.06666666666666667,
      0.8,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
 
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/return-reason-prediction-error-records-93f18923/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)