# Product Category Fixed-Holdout Learning Curve

> Product Category 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 product category text classification (apparel, home, electronics, other) using a naive-Bayes baseline on up to 200 labelled records.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-product-category-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/product-category-fixed-holdout-learning-curve-a08179b9
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_mNW7ZQZZN3NkFPwi0SuEM

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-fixed-holdout-learning-curve-a08179b9 -d '<json body>'
```

Example prompt: Run a fixed-holdout learning curve on my labelled product descriptions — I have records tagged as apparel, home, electronics, or other — and show me how the naive-Bayes baseline accuracy changes as more training data is used. Here are my 80 labelled examples.

## When to prefer this

Choose this endpoint when you need a quick, inexpensive learning curve evaluation for product category text classification using a local naive-Bayes baseline, especially when you have a small labelled dataset (up to 200 records) and want to understand how model performance scales with training size — without deploying a hosted model or fine-tuning a foundation model. Ideal for early-stage data quality checks, dataset sizing decisions, and lightweight ML benchmarking on the four product categories: apparel, home, electronics, and other.

## Known failure modes

- Fewer than 2 records supplied — minimum input not met
- Record text exceeds 1000-character limit — truncation or rejection
- Invalid or missing 'actual' label not in allowed enum — validation error
- Fewer than one example per class — class imbalance may prevent curve generation
- Dataset too small to produce meaningful holdout split — degenerate curve output
- Payment not processed — x402 payment required before response is returned

## How this service works

Product category - Fixed-holdout learning curve: Fixed-holdout learning curve 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 fixed-holdout learning curve deliverable.

## Output

Returns a fixed-holdout learning curve report showing how a naive-Bayes baseline classifier's performance metrics (such as accuracy or F1) vary as the number of training records increases, evaluated against a held-out portion of the supplied labelled dataset spanning categories: apparel, home, electronics, and other.

## 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": [
       "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"
    },
    {
     "id": "example-0-1",
     "text": "apparel sample document number 1 for this supplied dataset.",
     "actual": "apparel"
    },
    {
     "id": "example-0-2",
     "text": "apparel sample document number 2 for this supplied dataset.",
     "actual": "apparel"
    },
    {
     "id": "example-1-0",
     "text": "home sample document number 0 for this supplied dataset.",
     "actual": "home"
    },
    {
     "id": "example-1-1",
     "text": "home sample document number 1 for this supplied dataset.",
     "actual": "home"
    },
    {
     "id": "example-1-2",
     "text": "home sample document number 2 for this supplied dataset.",
     "actual": "home"
    },
    {
     "id": "example-2-0",
     "text": "electronics sample document number 0 for this supplied dataset.",
     "actual": "electronics"
    },
    {
     "id": "example-2-1",
     "text": "electronics sample document number 1 for this supplied dataset.",
     "actual": "electronics"
    },
    {
     "id": "example-2-2",
     "text": "electronics sample document number 2 for this supplied dataset.",
     "actual": "electronics"
    },
    {
     "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/product-category-fixed-holdout-learning-curve-a08179b9/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)