# Training Question Classifier — Macro, Micro & Weighted F1 Scores

> Training Question Classifier — Macro, Micro & Weighted F1 Scores is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).

Computes macro, micro, and weighted aggregate classification scores (precision, recall, F1) for a set of labelled training question records with actual vs. predicted categories.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-training-question-aggregate-scores?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-12
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/training-question-classifier-macro-micro-weighted-f1-scores-9d0f86d3
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Oud7xbjMcFWJ_Z_S3MbWx

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 training-question-classifier-macro-micro-weighted-f1-scores-9d0f86d3 -d '<json body>'
```

Example prompt: I have up to 200 labelled training question records — each with an actual label and a predicted label from my classifier (concept, procedure, example, or assessment) — can you compute the macro, micro, and weighted precision/recall/F1 scores so I can see how well my model is doing overall?

## When to prefer this

Choose this endpoint when you need lightweight, on-demand aggregate classification metrics (macro, micro, weighted F1) for a small labelled dataset of training question records categorised as concept, procedure, example, or assessment — without needing to host a model or use a foundation model training service. Ideal for rapid evaluation loops on datasets up to 200 records at minimal cost ($0.01 USDC).

## Known failure modes

- Too many records supplied (over 200 limit) — request rejected
- Missing required fields (id, text, actual, predicted) on any record — validation error
- Invalid label values outside the enum (concept, procedure, example, assessment) — schema error
- Empty records array — no scores can be computed
- Malformed JSON input — parse error

## How this service works

Training question - Macro micro and weighted scores: Macro micro and weighted scores for Training question. 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-averaged, micro-averaged, and weighted-averaged precision, recall, and F1 scores computed over the supplied labelled records, with class labels spanning concept, procedure, example, and assessment. The output is a bounded, single-deliverable evaluation summary for the provided dataset.

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/training-question-classifier-macro-micro-weighted-f1-scores-9d0f86d3/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)