# Training Question Confidence Calibration Bins

> Training Question Confidence Calibration Bins 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).

Computes confidence calibration bins for a text classifier that categorizes training questions into concept, procedure, example, or assessment classes.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-training-question-calibration?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/training-question-confidence-calibration-bins-d99037b2
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_K3VtSNjIln3UlFxccdI1U

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 training-question-confidence-calibration-bins-d99037b2 -d '<json body>'
```

Example prompt: Can you compute confidence calibration bins for my training question classifier? I have 50 labeled records where each has the actual label (concept, procedure, example, or assessment), the predicted label, and a 4-element probability vector — I want to see how well my model's confidence scores align with real accuracy.

## When to prefer this

Use this endpoint when you need lightweight, local confidence calibration analysis on up to 200 labeled records for a 4-class training question classifier (concept, procedure, example, assessment) without hosting a model or calling a foundation model API. Prefer it over general ML evaluation platforms when you want a quick, cheap ($0.01 USDC) calibration bin report on small datasets, especially in agentic pipelines that use x402 micropayment flows.

## Known failure modes

- Fewer than the minimum required records provided — may return empty or degenerate bins
- Probability vectors that don't sum to 1 or contain values outside [0,1] — validation error
- Records with missing required fields (id, text, actual, predicted, probabilities) — schema rejection
- More than 200 records supplied — truncation or rejection based on limit
- Mismatch between actual/predicted enum values and allowed classes — error

## How this service works

Training question - Confidence calibration bins: Confidence calibration bins 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 confidence calibration bins deliverable.

## Output

Returns confidence calibration bins showing how the classifier's predicted probabilities map to observed accuracy — typically bucketed probability ranges with the mean predicted confidence and actual accuracy per bin, enabling identification of over- or under-confidence across the concept, procedure, example, and assessment label classes.

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/training-question-confidence-calibration-bins-d99037b2/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)