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Feedback Theme Multiclass Brier Score
x402
Feedback Theme Multiclass Brier Score is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Computes the multiclass Brier score for a feedback theme classifier across up to 200 labelled text records with actual labels, predicted labels, and class probabilities.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feedback theme - Multiclass Brier score: Multiclass Brier score for Feedback theme. 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 brier score deliverable.
Output
Returns a single bounded multiclass Brier score value summarising the mean squared difference between predicted class probability vectors and one-hot encoded actual labels across all supplied records, allowing assessment of classifier calibration and probabilistic accuracy for the four feedback themes (quality, price, delivery, support).
Example use cases
Evaluating customer feedback classifier accuracy
“I built a feedback classifier that predicts whether a customer review is about quality, price, delivery, or support. I have 120 labelled examples with actual labels, predicted labels, and probability scores for each class — can you compute the multiclass Brier score to see how well-calibrated it is?”
Comparing naive-Bayes baseline to new model
“I want to benchmark my naive-Bayes baseline on 80 feedback records tagged as quality, price, delivery, or support. Each record has the actual label, the predicted label, and 4 class probabilities. Can you run a Brier score evaluation so I can compare it against my improved model?”
Validating probability calibration before deployment
“Before I deploy my feedback theme classifier, I need to check its probability calibration on 200 held-out records labelled with quality, price, delivery, or support categories. Each record has an ID, the original text, the actual and predicted labels, and a 4-class probability array — please calculate the multiclass Brier score.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-feedback-theme-brier?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a proper probabilistic scoring rule (Brier score) to evaluate a multiclass text classifier over the four feedback themes (quality, price, delivery, support) on a local labelled dataset of up to 200 records, particularly when you want to assess probability calibration rather than just accuracy. Prefer this over accuracy-only metrics when your classifier outputs class probabilities and you need a bounded, interpretable score. Best suited for small-scale offline evaluation workflows without requiring a hosted model or foundation model infrastructure.
Side effects
- More than 200 records submitted — exceeds maximum batch size
- Probability vector does not contain exactly 4 elements — violates minItems/maxItems constraint
- Individual probability values outside [0, 1] range — validation failure
- Invalid or unrecognised label value outside enum (quality, price, delivery, support) — schema rejection
- Empty records array submitted — no data to evaluate
- Missing required fields (id, text, actual, predicted, probabilities) on any record — schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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