# Return Reason Classifier – Fixed-Holdout Learning Curve

> Return Reason Classifier – Fixed-Holdout Learning Curve 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).

Generates a fixed-holdout learning curve for a naive-Bayes return-reason text classifier using up to 200 user-supplied labelled records, without any hosted or foundation-model training.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-learning-curve?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/return-reason-classifier-fixed-holdout-learning-curve-bf705bcd
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_wll7I4hAni3qOG-OabFHe

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-classifier-fixed-holdout-learning-curve-bf705bcd -d '<json body>'
```

Example prompt: Run a fixed-holdout learning curve for my return reason classifier using these 150 labelled customer messages — each tagged as damaged, wrong_item, unwanted, or other — and tell me how the naive-Bayes baseline accuracy changes as training size increases.

## When to prefer this

Choose this endpoint when you have a small labelled dataset (up to 200 records) of customer return reason texts and want a quick, low-cost learning curve analysis using a naive-Bayes baseline — particularly when you don't need a hosted model or foundation-model fine-tuning, and just want to understand how classifier performance scales with training data size for the four standard return reason categories.

## Known failure modes

- Fewer than 2 records supplied — minimum input violation
- Records missing required fields (id, text, actual) — schema validation error
- More than 200 records submitted — maxItems constraint exceeded
- Invalid 'actual' enum value (not one of: damaged, wrong_item, unwanted, other) — rejected
- Insufficient class diversity in records may produce degenerate learning curves
- Payment failure via x402 protocol — endpoint returns 402 status

## How this service works

Return reason - Fixed-holdout learning curve: Fixed-holdout learning curve 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 fixed-holdout learning curve deliverable.

## Output

A fixed-holdout learning curve deliverable showing how a naive-Bayes text classifier's performance metrics (e.g. accuracy) vary as the training set size increases, evaluated against a held-out portion of the supplied labelled records. Includes local baseline evaluation results without any hosted model or foundation-model training.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "records": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "id",
     "text",
     "actual"
    ],
    "properties": {
     "id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "text": {
      "type": "string",
      "default": "",
      "maxLength": 1000
     },
     "actual": {
      "enum": [
       "damaged",
       "wrong_item",
       "unwanted",
       "other"
      ],
      "type": "string"
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "id": "example-0-0",
     "text": "damaged sample document number 0 for this supplied dataset.",
     "actual": "damaged"
    },
    {
     "id": "example-0-1",
     "text": "damaged sample document number 1 for this supplied dataset.",
     "actual": "damaged"
    },
    {
     "id": "example-0-2",
     "text": "damaged sample document number 2 for this supplied dataset.",
     "actual": "damaged"
    },
    {
     "id": "example-1-0",
     "text": "wrong_item sample document number 0 for this supplied dataset.",
     "actual": "wrong_item"
    },
    {
     "id": "example-1-1",
     "text": "wrong_item sample document number 1 for this supplied dataset.",
     "actual": "wrong_item"
    },
    {
     "id": "example-1-2",
     "text": "wrong_item sample document number 2 for this supplied dataset.",
     "actual": "wrong_item"
    },
    {
     "id": "example-2-0",
     "text": "unwanted sample document number 0 for this supplied dataset.",
     "actual": "unwanted"
    },
    {
     "id": "example-2-1",
     "text": "unwanted sample document number 1 for this supplied dataset.",
     "actual": "unwanted"
    },
    {
     "id": "example-2-2",
     "text": "unwanted sample document number 2 for this supplied dataset.",
     "actual": "unwanted"
    },
    {
     "id": "example-3-0",
     "text": "other sample document number 0 for this supplied dataset.",
     "actual": "other"
    },
    {
     "id": "example-3-1",
     "text": "other sample document number 1 for this supplied dataset.",
     "actual": "other"
    },
    {
     "id": "example-3-2",
     "text": "other sample document number 2 for this supplied dataset.",
     "actual": "other"
    }
   ],
   "maxItems": 200,
   "minItems": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/return-reason-classifier-fixed-holdout-learning-curve-bf705bcd/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)