# Review Aspect Multiclass Log Loss Calculator

> Review Aspect 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 2026-10-15).

Computes multiclass log loss metric for review aspect classification (performance, appearance, value, other) from up to 200 labelled text records with predicted probabilities.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-review-aspect-log-loss?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/review-aspect-multiclass-log-loss-calculator-29b85fbc
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ZEDVbexOlmv8B9gB7R0C0

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 review-aspect-multiclass-log-loss-calculator-29b85fbc -d '<json body>'
```

Example prompt: Can you calculate the multiclass log loss for my review aspect classifier? I have 50 labelled records where each review is tagged as performance, appearance, value, or other — I'll provide the actual labels, predicted labels, and the 4-class probability vectors for each record.

## When to prefer this

Choose this endpoint when you need a quick, cheap ($0.01 USDC) multiclass log loss calculation specifically for review aspect classification (performance, appearance, value, other) on small datasets up to 200 records, without setting up local ML libraries or hosting an evaluation service. Ideal for lightweight model benchmarking, sanity-checking classifiers, or comparing baseline models in a review-domain NLP pipeline.

## Known failure modes

- More than 200 records supplied — request rejected or truncated
- Probability vectors not summing to 1 or containing values outside [0,1]
- Probability array length not exactly 4 — schema validation failure
- Invalid or unrecognised class label in actual or predicted fields
- Empty records array provided — no metric computable
- Missing required fields (id, text, actual, predicted, probabilities) on any record

## How this service works

Review aspect - Multiclass log loss: Multiclass log loss for Review aspect. 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

Returns a bounded multiclass log loss scalar value derived from the supplied labelled records, quantifying the divergence between predicted class probabilities and actual labels across the four review aspect categories (performance, appearance, value, other).

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/review-aspect-multiclass-log-loss-calculator-29b85fbc/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)