# Return Reason Classifier — Macro, Micro & Weighted Aggregate Scores

> Return Reason Classifier — Macro, Micro & Weighted Aggregate Scores 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 macro, micro, and weighted precision/recall/F1 aggregate scores for a return-reason text classification dataset with up to 200 labelled records.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-aggregate-scores?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/return-reason-classifier-macro-micro-weighted-aggregate-scores-1312aa0e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_x6lYKHqbzklUNkR1Cwt--

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-classifier-macro-micro-weighted-aggregate-scores-1312aa0e -d '<json body>'
```

Example prompt: Can you evaluate my return reason classifier? I have up to 200 labelled records where each record has an ID, the customer review text, the actual return category (damaged, wrong_item, unwanted, or other), and my model's predicted category — give me the macro, micro, and weighted precision/recall/F1 scores.

## When to prefer this

Choose this endpoint when you need fast, cheap ($0.01 USDC) aggregate classification metrics (macro, micro, weighted) for a bounded return-reason dataset without setting up a local ML evaluation environment. Ideal for lightweight model benchmarking or spot-checking classifier quality on up to 200 records in an agentic workflow where spinning up scikit-learn or a foundation model is impractical.

## Known failure modes

- More than 200 records supplied — request rejected or truncated
- Records missing required fields (id, text, actual, predicted) — validation error
- Invalid enum values in actual or predicted fields — schema rejection
- Empty records array — no metrics can be computed
- Malformed JSON payload — HTTP 400 bad request

## How this service works

Return reason - Macro micro and weighted scores: Macro micro and weighted scores 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 macro micro and weighted scores deliverable.

## Output

Returns macro, micro, and weighted aggregate classification metrics (precision, recall, F1) computed across the four return reason classes (damaged, wrong_item, unwanted, other) for the supplied labelled dataset of up to 200 records.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "records": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "id",
     "text",
     "actual",
     "predicted"
    ],
    "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"
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "id": "example-0-0",
     "text": "damaged sample document number 0 for this supplied dataset.",
     "actual": "damaged",
     "predicted": "damaged"
    },
    {
     "id": "example-0-1",
     "text": "damaged sample document number 1 for this supplied dataset.",
     "actual": "damaged",
     "predicted": "damaged"
    },
    {
     "id": "example-0-2",
     "text": "damaged sample document number 2 for this supplied dataset.",
     "actual": "damaged",
     "predicted": "damaged"
    },
    {
     "id": "example-1-0",
     "text": "wrong_item sample document number 0 for this supplied dataset.",
     "actual": "wrong_item",
     "predicted": "wrong_item"
    },
    {
     "id": "example-1-1",
     "text": "wrong_item sample document number 1 for this supplied dataset.",
     "actual": "wrong_item",
     "predicted": "wrong_item"
    },
    {
     "id": "example-1-2",
     "text": "wrong_item sample document number 2 for this supplied dataset.",
     "actual": "wrong_item",
     "predicted": "wrong_item"
    },
    {
     "id": "example-2-0",
     "text": "unwanted sample document number 0 for this supplied dataset.",
     "actual": "unwanted",
     "predicted": "unwanted"
    },
    {
     "id": "example-2-1",
     "text": "unwanted sample document number 1 for this supplied dataset.",
     "actual": "unwanted",
     "predicted": "unwanted"
    },
    {
     "id": "example-2-2",
     "text": "unwanted sample document number 2 for this supplied dataset.",
     "actual": "unwanted",
     "predicted": "unwanted"
    },
    {
     "id": "example-3-0",
     "text": "other sample document number 0 for this supplied dataset.",
     "actual": "other",
     "predicted": "other"
    },
    {
     
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/return-reason-classifier-macro-micro-weighted-aggregate-scores-1312aa0e/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)