# Feedback Theme Per-Label Precision and Recall

> Feedback Theme Per-Label Precision and Recall 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 per-label precision and recall metrics for a feedback theme classifier across up to 200 labelled text records with actual and predicted labels.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-feedback-theme-per-label?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-per-label-precision-and-recall-1b6dab8d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_m4LujhDSru2gTuR-aeXdd

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-per-label-precision-and-recall-1b6dab8d -d '<json body>'
```

Example prompt: I have 150 customer feedback records where each has the actual theme label (quality, price, delivery, or support) and what my classifier predicted — can you compute per-label precision and recall so I can see which categories my model struggles with?

## When to prefer this

Choose this endpoint when you need lightweight, bounded per-label precision and recall evaluation for a feedback theme classifier without requiring a hosted model or large-scale ML infrastructure. Ideal for small dataset spot-checks (up to 200 records), local evaluation pipelines, or naive Bayes baseline comparisons across the four feedback categories (quality, price, delivery, support). Not suitable for full model training, multi-class datasets beyond these four labels, or large-scale batch evaluation.

## Known failure modes

- More than 200 records submitted — input will be rejected or truncated
- Missing required fields (id, text, actual, predicted) in any record — validation error
- Invalid label values outside the enum (quality, price, delivery, support) — schema rejection
- Empty records array — no metrics computable
- All records belonging to one label only — some per-label metrics undefined or zero

## How this service works

Feedback theme - Per-label precision and recall: Per-label precision and recall 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 per-label precision and recall deliverable.

## Output

A per-label breakdown of precision and recall scores for each feedback theme category (quality, price, delivery, support), computed from the supplied actual vs. predicted label pairs across 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": [
       "quality",
       "price",
       "delivery",
       "support"
      ],
      "type": "string"
     },
     "predicted": {
      "enum": [
       "quality",
       "price",
       "delivery",
       "support"
      ],
      "type": "string"
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "id": "example-0-0",
     "text": "quality sample document number 0 for this supplied dataset.",
     "actual": "quality",
     "predicted": "quality"
    },
    {
     "id": "example-0-1",
     "text": "quality sample document number 1 for this supplied dataset.",
     "actual": "quality",
     "predicted": "quality"
    },
    {
     "id": "example-0-2",
     "text": "quality sample document number 2 for this supplied dataset.",
     "actual": "quality",
     "predicted": "quality"
    },
    {
     "id": "example-1-0",
     "text": "price sample document number 0 for this supplied dataset.",
     "actual": "price",
     "predicted": "price"
    },
    {
     "id": "example-1-1",
     "text": "price sample document number 1 for this supplied dataset.",
     "actual": "price",
     "predicted": "price"
    },
    {
     "id": "example-1-2",
     "text": "price sample document number 2 for this supplied dataset.",
     "actual": "price",
     "predicted": "price"
    },
    {
     "id": "example-2-0",
     "text": "delivery sample document number 0 for this supplied dataset.",
     "actual": "delivery",
     "predicted": "delivery"
    },
    {
     "id": "example-2-1",
     "text": "delivery sample document number 1 for this supplied dataset.",
     "actual": "delivery",
     "predicted": "delivery"
    },
    {
     "id": "example-2-2",
     "text": "delivery sample document number 2 for this supplied dataset.",
     "actual": "delivery",
     "predicted": "delivery"
    },
    {
     "id": "example-3-0",
     "text": "support sample document number 0 for this supplied dataset.",
     "actual": "support",
     "predicted": "support"
    },
    {
     "id": "example-3-1",
     "text": "support sa
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feedback-theme-per-label-precision-and-recall-1b6dab8d/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)