# Knowledge Topic Classifier — Macro, Micro & Weighted Scores

> Knowledge Topic Classifier — Macro, Micro & Weighted 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 scores for a knowledge-topic text classifier across four categories: setup, usage, troubleshooting, and reference.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-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/knowledge-topic-classifier-macro-micro-weighted-scores-954198e7
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_GgSpY1BYRTz2bvy1NKayB

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 knowledge-topic-classifier-macro-micro-weighted-scores-954198e7 -d '<json body>'
```

Example prompt: Evaluate my knowledge topic classifier by computing macro, micro, and weighted F1 scores — here are up to 200 labelled records, each with an ID, the text, the actual category (setup, usage, troubleshooting, or reference), and what my model predicted.

## When to prefer this

Choose this endpoint when you need a quick, cheap ($0.01 USDC) evaluation of a knowledge-topic text classifier that categorizes documents into setup, usage, troubleshooting, and reference — particularly when you already have predicted and actual labels and want macro/micro/weighted aggregate scores without setting up a local evaluation pipeline.

## Known failure modes

- Empty records array returns error or degenerate metrics
- Records exceeding 200 items may be truncated or rejected
- Invalid or missing 'actual'/'predicted' enum values cause validation errors
- Imbalanced classes may produce misleading macro averages
- Missing required fields (id, text, actual, predicted) cause schema validation failures

## How this service works

Knowledge topic - Macro micro and weighted scores: Macro micro and weighted scores for Knowledge 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 macro micro and weighted scores deliverable.

## Output

Returns aggregate classification metrics — macro, micro, and weighted precision, recall, and F1 scores — computed over up to 200 supplied records with actual and predicted labels across four knowledge topic categories: setup, usage, troubleshooting, and reference.

## 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": [
       "setup",
       "usage",
       "troubleshooting",
       "reference"
      ],
      "type": "string"
     },
     "predicted": {
      "enum": [
       "setup",
       "usage",
       "troubleshooting",
       "reference"
      ],
      "type": "string"
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "id": "example-0-0",
     "text": "setup sample document number 0 for this supplied dataset.",
     "actual": "setup",
     "predicted": "setup"
    },
    {
     "id": "example-0-1",
     "text": "setup sample document number 1 for this supplied dataset.",
     "actual": "setup",
     "predicted": "setup"
    },
    {
     "id": "example-0-2",
     "text": "setup sample document number 2 for this supplied dataset.",
     "actual": "setup",
     "predicted": "setup"
    },
    {
     "id": "example-1-0",
     "text": "usage sample document number 0 for this supplied dataset.",
     "actual": "usage",
     "predicted": "usage"
    },
    {
     "id": "example-1-1",
     "text": "usage sample document number 1 for this supplied dataset.",
     "actual": "usage",
     "predicted": "usage"
    },
    {
     "id": "example-1-2",
     "text": "usage sample document number 2 for this supplied dataset.",
     "actual": "usage",
     "predicted": "usage"
    },
    {
     "id": "example-2-0",
     "text": "troubleshooting sample document number 0 for this supplied dataset.",
     "actual": "troubleshooting",
     "predicted": "troubleshooting"
    },
    {
     "id": "example-2-1",
     "text": "troubleshooting sample document number 1 for this supplied dataset.",
     "actual": "troubleshooting",
     "predicted": "troubleshooting"
    },
    {
     "id": "example-2-2",
     "text": "troubleshooting sample document number 2 for this supplied dataset.",
     "actual": "troubleshooting",
     "predicted": "troubleshooting"
    },
    {
     "id": "example-3-0",
     "text": "reference sample document number 0 for this supplied dataset.",
     "actual": "reference",
     "predicted": "reference
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/knowledge-topic-classifier-macro-micro-weighted-scores-954198e7/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)