# Training Question Multiclass Log Loss Calculator

> Training Question Multiclass Log Loss Calculator is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).

Computes multiclass log loss for up to 200 labelled text records across four training question categories (concept, procedure, example, assessment)

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-training-question-log-loss?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/training-question-multiclass-log-loss-calculator-e2fb1e37
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_d3OM-Zm5JRFne6tNvEsrK

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-multiclass-log-loss-calculator-e2fb1e37 -d '<json body>'
```

Example prompt: Calculate the multiclass log loss for my training question classifier — here are 12 labelled records with actual and predicted classes (concept, procedure, example, assessment) and their probability vectors, and I need the bounded log loss score back.

## When to prefer this

Choose this endpoint when you need a fast, cheap ($0.01 USDC) multiclass log loss computation specifically for text classification tasks involving the training question taxonomy (concept, procedure, example, assessment). It is ideal for small local evaluations of up to 200 records without needing a hosted model or large foundation-model infrastructure. Prefer this over general ML platforms when you want a lightweight, per-call scoring primitive that fits into an agent workflow for iterative model evaluation.

## Known failure modes

- More than 200 records submitted — request exceeds batch limit
- Probability arrays do not contain exactly 4 values per record — schema validation failure
- Probability values outside [0,1] range — rejected by schema constraints
- Actual or predicted labels not in allowed enum (concept, procedure, example, assessment) — validation error
- Empty records array — no computable loss
- Record ID missing or exceeding 80 character limit — schema rejection
- Payment not included or insufficient — x402 payment required error

## How this service works

Training question - Multiclass log loss: Multiclass log loss 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 multiclass log loss deliverable.

## Output

A bounded multiclass log loss value computed over the supplied labelled records. The response captures how well predicted probability distributions align with actual class labels across up to four categories (concept, procedure, example, assessment), returning a single deliverable numeric loss score.

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

## More

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