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Release Change Multiclass Log Loss Calculator
x402
Release Change 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 release-change text classification (feature/fix/documentation/maintenance) given labeled records with predicted probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Release change - Multiclass log loss: Multiclass log loss for Release change. 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 value computed over the supplied labeled text records, indicating the quality of probabilistic predictions across the four release-change categories (feature, fix, documentation, maintenance). Lower values indicate better-calibrated predictions.
Example use cases
Evaluating a release tagger model
“I've got 120 labeled changelog entries tagged as feature, fix, documentation, or maintenance, and my model output probability scores for each. Can you compute the multiclass log loss so I can see how well the model is calibrated?”
Comparing baseline vs tuned classifier
“I want to benchmark my naive-Bayes baseline on release change classification — here are 80 records with actual labels and predicted probabilities across all four categories. Give me the log loss so I can compare it to my fine-tuned model.”
Detecting poorly calibrated predictions
“My release change classifier seems overconfident on 'fix' labels. Can you compute the log loss across these 200 labeled records with their probability outputs to quantify how poorly calibrated it is?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-release-change-log-loss?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to evaluate a text classifier on software release change categorization tasks using the standard multiclass log loss metric. It is especially suitable for small datasets (up to 200 records) and works well for local evaluation pipelines, baseline benchmarking, and model calibration checks across the four canonical release change types.
Side effects
- Probabilities array does not sum to 1 or contains values outside [0,1]
- Records array exceeds 200 item limit
- Missing required fields (id, text, actual, predicted, probabilities)
- Invalid class label not in enum [feature, fix, documentation, maintenance]
- Empty records array submitted
- Probabilities array does not have exactly 4 elements
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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