# Return Reason Multiclass Brier Score Calculator

> Return Reason Multiclass Brier Score Calculator is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).

Computes the multiclass Brier score for a return-reason classifier given up to 200 labelled text records with predicted probabilities.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-brier?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-18
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/return-reason-multiclass-brier-score-calculator-e6caa87c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_t3Jys_UyJsvTcitmvdYr8

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 return-reason-multiclass-brier-score-calculator-e6caa87c -d '<json body>'
```

Example prompt: Calculate the multiclass Brier score for my return-reason classifier — I have 50 labelled records where each record has the actual class (damaged, wrong_item, unwanted, or other), my model's predicted class, and a 4-element probability vector; can you run this evaluation and tell me how well my probabilities are calibrated?

## When to prefer this

Choose this endpoint when you need a cheap, fast, bounded multiclass Brier score specifically for the four return-reason classes (damaged, wrong_item, unwanted, other) without standing up your own evaluation infrastructure. It is ideal for quick calibration checks, CI/CD quality gates, or comparing model versions on small batches of up to 200 records. It is not suitable for large-scale offline batch evaluation, arbitrary class taxonomies, or use cases requiring full confusion matrices or other metrics beyond the Brier score.

## Known failure modes

- More than 200 records supplied — request rejected or truncated
- Probability vectors that do not sum to 1 or contain values outside [0,1] — validation error
- Mismatched enum value in actual/predicted fields — schema validation error
- Empty records array — returns error or degenerate score
- Fewer than 4 probabilities in a probability vector — schema error

## How this service works

Return reason - Multiclass Brier score: Multiclass Brier score for Return reason. 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 multiclass brier score deliverable.

## Output

Returns a single multiclass Brier score (a float between 0 and 2, where 0 is perfect) summarising the mean squared error between predicted probability vectors and one-hot actual labels across all supplied records and the four return-reason classes (damaged, wrong_item, unwanted, other).

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/return-reason-multiclass-brier-score-calculator-e6caa87c/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)