# Meeting Topic Classifier — Confidence Calibration Bins

> Meeting Topic Classifier — 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 2026-10-15).

Computes confidence calibration bins for a meeting topic classifier (planning/progress/decision/review) given up to 200 labelled records with predicted probabilities.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-meeting-topic-calibration?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/meeting-topic-classifier-confidence-calibration-bins-d0d600c4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_mYGPR2kcDJ3qCwfNVYwMG

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 meeting-topic-classifier-confidence-calibration-bins-d0d600c4 -d '<json body>'
```

Example prompt: I have predictions from my meeting topic classifier — each record has the actual label (planning, progress, decision, or review), the predicted label, and the four class probabilities. Can you compute confidence calibration bins across these 50 labelled records so I can see where my model is overconfident or underconfident?

## When to prefer this

Choose this endpoint when you need to evaluate the probability calibration quality of a meeting topic classifier (planning/progress/decision/review) using your own labelled dataset. Ideal for post-training evaluation, pre-deployment checks, or comparing model versions. Prefer this over generic calibration tools when working specifically in the meeting transcript/topic classification domain with four defined classes.

## Known failure modes

- Fewer than the minimum required records provided — returns error or empty bins
- Probabilities array does not sum to 1 or contains values outside [0,1] — validation error
- More than 200 records submitted — request rejected or truncated
- Mismatched actual/predicted enum values outside allowed set — schema validation failure
- Payment not included or insufficient — 402 payment required response

## How this service works

Meeting topic - Confidence calibration bins: Confidence calibration bins for Meeting topic. 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 for the meeting topic classifier: binned breakdowns showing mean predicted confidence versus actual accuracy per bin, enabling detection of overconfidence or underconfidence across the planning, progress, decision, and review 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": [
       "planning",
       "progress",
       "decision",
       "review"
      ],
      "type": "string"
     },
     "predicted": {
      "enum": [
       "planning",
       "progress",
       "decision",
       "review"
      ],
      "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": "planning sample document number 0 for this supplied dataset.",
     "actual": "planning",
     "predicted": "planning",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-0-1",
     "text": "planning sample document number 1 for this supplied dataset.",
     "actual": "planning",
     "predicted": "planning",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-0-2",
     "text": "planning sample document number 2 for this supplied dataset.",
     "actual": "planning",
     "predicted": "planning",
     "probabilities": [
      0.8,
      0.06666666666666667,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-0",
     "text": "progress sample document number 0 for this supplied dataset.",
     "actual": "progress",
     "predicted": "progress",
     "probabilities": [
      0.06666666666666667,
      0.8,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    {
     "id": "example-1-1",
     "text": "progress sample document number 1 for this supplied dataset.",
     "actual": "progress",
     "predicted": "progress",
     "probabilities": [
      0.06666666666666667,
      0.8,
      0.06666666666666667,
      0.06666666666666667
     ]
    },
    
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/meeting-topic-classifier-confidence-calibration-bins-d0d600c4/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)