# Fixed-Holdout Learning Curve for Training Question Classification

> Fixed-Holdout Learning Curve for Training Question Classification is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).

Generates a fixed-holdout learning curve analysis for a naive-Bayes text classifier trained to categorize training content as concept, procedure, example, or assessment.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-training-question-learning-curve?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-12
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/fixed-holdout-learning-curve-for-training-question-classification-e66ebef2
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_a0WNdpuuQyEAOQgi-Zy77

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 fixed-holdout-learning-curve-for-training-question-classification-e66ebef2 -d '<json body>'
```

Example prompt: Run a fixed-holdout learning curve on my labelled training question dataset — I have about 80 records tagged as concept, procedure, example, or assessment, and I want to see how the naive-Bayes classifier accuracy changes as training size grows.

## When to prefer this

Choose this endpoint when you have up to 200 labelled text records in the training question domain (concept/procedure/example/assessment) and need a quick, cheap ($0.01 USDC) learning curve evaluation using a naive-Bayes baseline without requiring a hosted model or expensive foundation model API. Best suited for feasibility checks, dataset sizing decisions, and lightweight ML benchmarking on educational content classification tasks.

## Known failure modes

- Fewer than 2 records supplied — minimum record count not met
- Records missing required fields (id, text, actual) — validation error
- Class label not in allowed enum (concept, procedure, example, assessment) — rejected record
- More than 200 records supplied — exceeds maximum input size
- Insufficient class representation to form meaningful holdout split — degenerate curve

## How this service works

Training question - Fixed-holdout learning curve: Fixed-holdout learning curve for Training question. 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 deliverable showing how naive-Bayes classifier accuracy on training question categories (concept, procedure, example, assessment) varies with training set size, computed from up to 200 supplied labelled text records using local evaluation tools and no hosted or foundation model.

## 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": [
       "concept",
       "procedure",
       "example",
       "assessment"
      ],
      "type": "string"
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "id": "example-0-0",
     "text": "concept sample document number 0 for this supplied dataset.",
     "actual": "concept"
    },
    {
     "id": "example-0-1",
     "text": "concept sample document number 1 for this supplied dataset.",
     "actual": "concept"
    },
    {
     "id": "example-0-2",
     "text": "concept sample document number 2 for this supplied dataset.",
     "actual": "concept"
    },
    {
     "id": "example-1-0",
     "text": "procedure sample document number 0 for this supplied dataset.",
     "actual": "procedure"
    },
    {
     "id": "example-1-1",
     "text": "procedure sample document number 1 for this supplied dataset.",
     "actual": "procedure"
    },
    {
     "id": "example-1-2",
     "text": "procedure sample document number 2 for this supplied dataset.",
     "actual": "procedure"
    },
    {
     "id": "example-2-0",
     "text": "example sample document number 0 for this supplied dataset.",
     "actual": "example"
    },
    {
     "id": "example-2-1",
     "text": "example sample document number 1 for this supplied dataset.",
     "actual": "example"
    },
    {
     "id": "example-2-2",
     "text": "example sample document number 2 for this supplied dataset.",
     "actual": "example"
    },
    {
     "id": "example-3-0",
     "text": "assessment sample document number 0 for this supplied dataset.",
     "actual": "assessment"
    },
    {
     "id": "example-3-1",
     "text": "assessment sample document number 1 for this supplied dataset.",
     "actual": "assessment"
    },
    {
     "id": "example-3-2",
     "text": "assessment sample document number 2 for this supplied dataset.",
     "actual": "assessment"
    }
   ],
   "maxItems": 200,
   "minItems": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/fixed-holdout-learning-curve-for-training-question-classification-e66ebef2/health.json
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