# Feedback Theme Confidence Calibration Bins

> Feedback Theme 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 2026-10-19).

Computes confidence calibration bins for a naive-Bayes feedback theme classifier across quality, price, delivery, and support categories using up to 200 labelled text records.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-feedback-theme-calibration?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feedback-theme-confidence-calibration-bins-011125c3
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Ol94sYbJA82IGob3RKASt

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 feedback-theme-confidence-calibration-bins-011125c3 -d '<json body>'
```

Example prompt: Can you compute confidence calibration bins for my feedback theme classifier? I have 120 labelled records with actual and predicted labels across quality, price, delivery, and support categories, plus the 4-class probability scores for each — I want to see how well the model's confidence scores are actually calibrated.

## When to prefer this

Choose this endpoint when you need to evaluate the probability calibration of a 4-class feedback theme classifier (quality, price, delivery, support) using a small local dataset of up to 200 labelled examples. It is ideal for post-hoc model evaluation without requiring a hosted model or foundation model. Prefer it over generic ML APIs when you specifically need calibration bin statistics rather than raw accuracy metrics, and when your classifier is a naive-Bayes or similar baseline model on customer feedback categories.

## Known failure modes

- Fewer than the minimum required records submitted — returns error indicating insufficient data
- Probability arrays not summing to 1 or containing values outside [0,1] — validation error
- Mismatched enum values in actual/predicted fields — schema rejection
- More than 200 records submitted — truncation or rejection depending on implementation
- Empty records array — error with no bins returned

## How this service works

Feedback theme - Confidence calibration bins: Confidence calibration bins for Feedback theme. 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 bin data for the submitted labelled dataset: probability ranges are grouped into bins, and within each bin the endpoint reports the mean predicted confidence versus the observed accuracy, enabling reliability diagram construction and detection of over- or under-confidence in the classifier.

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feedback-theme-confidence-calibration-bins-011125c3/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)