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Return Reason Multiclass Log Loss Calculator
x402
Return Reason 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 for return-reason text classification models across up to 200 labelled records
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Multiclass log loss: Multiclass log loss for Return reason. 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 single bounded multiclass log loss value (cross-entropy) computed over all supplied labelled records, reflecting how well the predicted probability distributions match the actual return-reason classes (damaged, wrong_item, unwanted, other). Lower values indicate better-calibrated predictions.
Example use cases
Validate return classifier before deployment
“Before I push my return reason model to production, can you run a multiclass log loss evaluation on these 120 labelled records? Each record has the customer return text, the actual reason (damaged, wrong_item, unwanted, or other), the model's predicted label, and the four class probabilities it output.”
Compare two model versions on return data
“I want to compare two versions of my return reason classifier — can you compute the multiclass log loss for version A's predictions? I have 80 records with the actual return reason labels and the predicted probabilities for each of the four classes: damaged, wrong_item, unwanted, and other.”
Audit naive-Bayes baseline for returns triage
“We're testing a naive-Bayes baseline for triaging customer returns. Can you calculate the multiclass log loss for 200 records where each has the true return reason label and the model's predicted probability distribution across damaged, wrong_item, unwanted, and other?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-log-loss?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cheap ($0.01 USDC) multiclass log loss evaluation specifically for return-reason text classification with exactly four classes (damaged, wrong_item, unwanted, other) on up to 200 records, without needing to host a model or use a foundation model. Best suited for local evaluation, model benchmarking, and naive-Bayes baseline assessment in e-commerce return workflows.
Side effects
- More than 200 records supplied — request will be rejected or truncated
- Probability vectors that do not sum to 1 or contain values outside [0,1]
- Missing required fields (id, text, actual, predicted, probabilities) on any record
- Probabilities array length not exactly 4
- Invalid enum value for actual or predicted class
- Empty records array
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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